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chore: rename libs→common_libs, all bot dirs to _garage suffix, fix all path refs

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Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Davide Cappellini
2026-08-27 18:18:41 +02:00
parent f8c0c871c6
commit b509195ee9
832 changed files with 4967 additions and 368 deletions
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SAC_LSTM_Bot_garage/SAC_LSTM_Bot
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SAC_LSTM_Bot_garage/SAC_LSTM_Bot.json
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{
"name": "SAC_LSTM_Bot",
"version": "0.1.0",
"authors": ["Davide Cappellini"],
"description": "SAC+LSTM-trained Tank Royale bot (#37) — training/eval launch config",
"homepage": "",
"countryCodes": ["IT"],
"gameTypes": ["classic", "melee", "1v1"],
"platform": "Nim",
"programmingLang": "Nim"
}
SAC_LSTM_Bot_garage/SAC_LSTM_Bot.nimble
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# Package
version = "0.1.0"
author = "Davide Cappellini"
description = "SAC+LSTM-trained Tank Royale bot"
license = "MIT"
srcDir = "src"
bin = @["SAC_LSTM_Bot"]
# Dependencies
requires "nim >= 2.0.0"
# tankroyale_botapi is vendored in-tree (common_libs/tankroyale_botapi) and wired via
# config.nims --path; no nimble dependency so builds never touch ~/.nimble/pkgs2.
requires "arraymancer >= 0.7.0"
SAC_LSTM_Bot_garage/SAC_LSTM_Bot.sh
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#!/bin/sh
# Launch config for tools/training_runner/RunTraining.java (#49): the runner
# executes <json-basename>.sh inside the bot dir (sample-bots convention).
exec "$(dirname "$0")/SAC_LSTM_Bot"
SAC_LSTM_Bot_garage/campaign_v4_stdout.log.pre-merge
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=== SAC_LSTM_Bot training harness ===
Opponents: Corners:3,Crazy:2,RamFire:2,Target:1,SacTwin:1 | budget: 100 rounds in chunks of 10
Eval: every 2 chunks, 10 rounds vs [Corners,Crazy,Target], MA-5 composite best-gating
Weights: /home/davide/Projects/SirRoboGarage/SAC_LSTM_Bot/weights/sac_latest.zip
Building SAC_LSTM_Bot/SAC_LSTM_Bot using c backend
Info: compiling nim package using /nix/store/pq494yyknvhrvkzna9zgcqvma8fq3617-x86_64-unknown-linux-gnu-nim-wrapper-2.2.4/bin/x86_64-unknown-linux-gnu-nim
=== Chunk 1/10: 10 rounds vs Corners ===
Training: SAC_LSTM_Bot vs Corners for 10 rounds
Log: /home/davide/Projects/SirRoboGarage/SAC_LSTM_Bot/training_log.jsonl
Round 1/10 — ticks:684 score:0 win:false
Round 2/10 — ticks:631 score:0 win:false
Round 3/10 — ticks:738 score:0 win:false
Round 4/10 — ticks:920 score:31 win:false
Round 5/10 — ticks:682 score:0 win:false
Round 6/10 — ticks:650 score:0 win:false
Round 7/10 — ticks:988 score:0 win:false
Round 8/10 — ticks:365 score:2 win:false
Round 9/10 — ticks:481 score:32 win:false
Round 10/10 — ticks:703 score:16 win:false
Counter check passed: 2291 == expected 2291
=== Chunk 2/10: 10 rounds vs Crazy ===
Training: SAC_LSTM_Bot vs Crazy for 10 rounds
Log: /home/davide/Projects/SirRoboGarage/SAC_LSTM_Bot/training_log.jsonl
Round 1/10 — ticks:667 score:17 win:false
Round 2/10 — ticks:709 score:0 win:false
Round 3/10 — ticks:856 score:49 win:false
Round 4/10 — ticks:794 score:2 win:false
Round 5/10 — ticks:791 score:1 win:false
Round 6/10 — ticks:936 score:19 win:false
Round 7/10 — ticks:625 score:3 win:false
Round 8/10 — ticks:977 score:21 win:false
Round 9/10 — ticks:658 score:1 win:false
Round 10/10 — ticks:825 score:1 win:false
Counter check passed: 2301 == expected 2301
>>> [eval] 10 deterministic rounds vs Corners
Training: SAC_LSTM_Bot vs Corners for 10 rounds
Log: /home/davide/Projects/SirRoboGarage/SAC_LSTM_Bot/eval_log.jsonl.Corners.tmp
Round 1/10 — ticks:3657 score:0 win:false
Round 2/10 — ticks:3007 score:60 win:false
Round 3/10 — ticks:3786 score:0 win:false
Round 4/10 — ticks:1312 score:5 win:false
Round 5/10 — ticks:1310 score:2 win:false
Round 6/10 — ticks:1668 score:0 win:false
Round 7/10 — ticks:1290 score:0 win:false
Round 8/10 — ticks:1537 score:1 win:false
Round 9/10 — ticks:1201 score:0 win:false
Round 10/10 — ticks:4406 score:60 win:false
Counter check passed: 2311 == expected 2311
>>> [eval] win rate: 0/10 (0%) vs Corners
>>> [eval] 10 deterministic rounds vs Crazy
Training: SAC_LSTM_Bot vs Crazy for 10 rounds
Log: /home/davide/Projects/SirRoboGarage/SAC_LSTM_Bot/eval_log.jsonl.Crazy.tmp
Round 1/10 — ticks:1898 score:1 win:false
Round 2/10 — ticks:1327 score:11 win:false
Round 3/10 — ticks:1669 score:5 win:false
Round 4/10 — ticks:945 score:61 win:false
Round 5/10 — ticks:1775 score:67 win:false
Round 6/10 — ticks:1452 score:5 win:false
Round 7/10 — ticks:1002 score:0 win:false
Round 8/10 — ticks:2124 score:64 win:false
Round 9/10 — ticks:1460 score:1 win:false
Round 10/10 — ticks:935 score:61 win:false
Counter check passed: 2321 == expected 2321
>>> [eval] win rate: 0/10 (0%) vs Crazy
>>> [eval] 10 deterministic rounds vs Target
Training: SAC_LSTM_Bot vs Target for 10 rounds
Log: /home/davide/Projects/SirRoboGarage/SAC_LSTM_Bot/eval_log.jsonl.Target.tmp
Round 1/10 — ticks:992 score:1 win:false
Round 2/10 — ticks:1442 score:60 win:true
Round 3/10 — ticks:1002 score:17 win:false
Round 4/10 — ticks:1026 score:1 win:false
Round 5/10 — ticks:992 score:7 win:false
Round 6/10 — ticks:1422 score:60 win:false
Round 7/10 — ticks:964 score:9 win:false
Round 8/10 — ticks:1398 score:1 win:false
Round 9/10 — ticks:1014 score:4 win:false
Round 10/10 — ticks:1435 score:60 win:false
Counter check passed: 2331 == expected 2331
>>> [eval] win rate: 1/10 (10%) vs Target
=== Chunk 3/10: 10 rounds vs Target ===
Training: SAC_LSTM_Bot vs Target for 10 rounds
Log: /home/davide/Projects/SirRoboGarage/SAC_LSTM_Bot/training_log.jsonl
Round 1/10 — ticks:1012 score:26 win:false
Round 2/10 — ticks:653 score:0 win:false
Round 3/10 — ticks:650 score:0 win:false
Round 4/10 — ticks:676 score:0 win:false
Round 5/10 — ticks:651 score:0 win:false
Round 6/10 — ticks:931 score:22 win:false
Round 7/10 — ticks:954 score:6 win:false
Round 8/10 — ticks:670 score:0 win:false
Round 9/10 — ticks:722 score:0 win:false
Round 10/10 — ticks:897 score:21 win:false
Counter check passed: 2341 == expected 2341
=== Chunk 4/10: 10 rounds vs SacTwin ===
Training: SAC_LSTM_Bot vs SacTwin for 10 rounds
Log: /home/davide/Projects/SirRoboGarage/SAC_LSTM_Bot/training_log.jsonl
Round 1/10 — ticks:821 score:0 win:false
Round 2/10 — ticks:837 score:3 win:false
Round 3/10 — ticks:858 score:4 win:false
Round 4/10 — ticks:934 score:14 win:false
Round 5/10 — ticks:1058 score:36 win:false
Round 6/10 — ticks:1241 score:76 win:false
Round 7/10 — ticks:958 score:16 win:false
Round 8/10 — ticks:940 score:33 win:false
Round 9/10 — ticks:707 score:0 win:false
Round 10/10 — ticks:689 score:0 win:false
Counter check passed: 2351 == expected 2351
>>> [eval] 10 deterministic rounds vs Corners
Training: SAC_LSTM_Bot vs Corners for 10 rounds
Log: /home/davide/Projects/SirRoboGarage/SAC_LSTM_Bot/eval_log.jsonl.Corners.tmp
Round 1/10 — ticks:1647 score:0 win:false
Round 2/10 — ticks:1518 score:0 win:false
Round 3/10 — ticks:1733 score:0 win:false
Round 4/10 — ticks:1731 score:0 win:false
Round 5/10 — ticks:1077 score:17 win:false
Round 6/10 — ticks:1745 score:0 win:false
Round 7/10 — ticks:1634 score:0 win:false
Round 8/10 — ticks:1710 score:1 win:false
Round 9/10 — ticks:2180 score:60 win:false
Round 10/10 — ticks:1364 score:0 win:false
Counter check passed: 2361 == expected 2361
>>> [eval] win rate: 0/10 (0%) vs Corners
>>> [eval] 10 deterministic rounds vs Crazy
Training: SAC_LSTM_Bot vs Crazy for 10 rounds
Log: /home/davide/Projects/SirRoboGarage/SAC_LSTM_Bot/eval_log.jsonl.Crazy.tmp
Round 1/10 — ticks:1426 score:68 win:true
Round 2/10 — ticks:1649 score:70 win:true
Round 3/10 — ticks:1456 score:64 win:true
Round 4/10 — ticks:1032 score:61 win:true
Round 5/10 — ticks:1710 score:1 win:true
Round 6/10 — ticks:941 score:61 win:true
Round 7/10 — ticks:1455 score:61 win:true
Round 8/10 — ticks:1323 score:66 win:true
Round 9/10 — ticks:1035 score:5 win:true
Round 10/10 — ticks:1457 score:65 win:true
Counter check passed: 2371 == expected 2371
>>> [eval] win rate: 10/10 (100%) vs Crazy
>>> [eval] 10 deterministic rounds vs Target
Training: SAC_LSTM_Bot vs Target for 10 rounds
Log: /home/davide/Projects/SirRoboGarage/SAC_LSTM_Bot/eval_log.jsonl.Target.tmp
Round 1/10 — ticks:1014 score:14 win:false
Round 2/10 — ticks:1074 score:20 win:false
Round 3/10 — ticks:996 score:6 win:false
Round 4/10 — ticks:1451 score:0 win:false
Round 5/10 — ticks:970 score:25 win:false
Round 6/10 — ticks:1451 score:0 win:false
Round 7/10 — ticks:1044 score:1 win:false
Round 8/10 — ticks:1119 score:13 win:false
Round 9/10 — ticks:1184 score:15 win:false
Round 10/10 — ticks:1028 score:9 win:false
Counter check passed: 2381 == expected 2381
>>> [eval] win rate: 0/10 (0%) vs Target
=== Chunk 5/10: 10 rounds vs Crazy ===
Training: SAC_LSTM_Bot vs Crazy for 10 rounds
Log: /home/davide/Projects/SirRoboGarage/SAC_LSTM_Bot/training_log.jsonl
Round 1/10 — ticks:882 score:0 win:false
Round 2/10 — ticks:865 score:0 win:false
Round 3/10 — ticks:908 score:0 win:false
Round 4/10 — ticks:720 score:1 win:false
Round 5/10 — ticks:832 score:17 win:false
Round 6/10 — ticks:957 score:16 win:false
Round 7/10 — ticks:723 score:3 win:false
Round 8/10 — ticks:645 score:0 win:false
Round 9/10 — ticks:963 score:0 win:false
Round 10/10 — ticks:1032 score:16 win:false
Counter check passed: 2391 == expected 2391
=== Chunk 6/10: 10 rounds vs Corners ===
Training: SAC_LSTM_Bot vs Corners for 10 rounds
Log: /home/davide/Projects/SirRoboGarage/SAC_LSTM_Bot/training_log.jsonl
Round 1/10 — ticks:818 score:0 win:false
Round 2/10 — ticks:905 score:0 win:false
Round 3/10 — ticks:851 score:0 win:false
Round 4/10 — ticks:381 score:32 win:false
SAC_LSTM_Bot_garage/campaign_v4_stdout.log.prereboot-20260824T1033
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SAC_LSTM_Bot_garage/config.nims
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# Static-link OpenBLAS for portable deployment
# ponytail: adjust path per machine, or use pkg-config
switch("passL", "-L/nix/store/v07svn2y92bvzjl51aj7c9ca1cwg7rw7-openblas-0.3.32/lib -lopenblas")
# libzip for weight checkpoint zip files
# ponytail: nix store path; adjust per machine, or use pkg-config
switch("passL", "-L/nix/store/wqvz31s598bvj3zb747943xhl38hjc6h-libzip-1.11.4/lib -lzip")
switch("threads", "on")
# Submodules import each other as SAC_LSTM_Bot/<mod>; make that resolvable for
# the binary build too (tests already add ../src via tests/config.nims).
switch("path", thisDir() & "/src")
# begin Nimble config (version 2)
when withDir(thisDir(), system.fileExists("nimble.paths")):
include "nimble.paths"
# end Nimble config
# Use the repo-vendored Tank Royale bot API (common_libs/) instead of the nimble pkg.
# Must come AFTER the nimble.paths include: later --path wins the import search.
switch("path", thisDir() & "/../common_libs/tankroyale_botapi")
switch("path", thisDir() & "/../common_libs/radar_lock")
SAC_LSTM_Bot_garage/docs/bozza_requisiti.md
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# Implementazione Agente DRL Recurrent-SAC (SAC-GRU) Nativo per Robocode TankRoyale
Bot nativo basato sull'algoritmo **Recurrent Soft Actor-Critic (SAC-GRU)** addestrato per combattere nell'ambiente **Robocode TankRoyale**.
Il bot deve connettersi direttamente al server WebSocket di TankRoyale tramite un'interfaccia client API nativa, senza l'uso di middleware o socket intermediari extra. Il sistema deve garantire il rispetto del limite rigido di **30 ms per turno** imposto dal simulatore, eseguendo l'inferenza e l'addestramento in modo concorrente all'interno dello stesso processo.
---
## 1. Architettura Multi-Thread e Concorrenza (30 ms Tick Constraint)
Per evitare il fenomeno degli "skipped turns", l'applicazione deve essere suddivisa in due thread principali coordinati nello stesso processo:
1. **Thread 1: Realtime Async Event Loop (WebSocket Client)**
* Riceve i messaggi di evento dal server TankRoyale via WebSocket ad ogni tick.
* Estragga e normalizza il vettore di stato a 17 dimensioni ($S_t$).
* Esegue l'inferenza rapida dell'Actor ($< 2 \text{ ms}$) fornendo lo stato $S_t$ e lo stato nascosto corrente della GRU ($h_{t-1}$).
* Mappa le azioni restituite e invia immediatamente il `BotIntent` al server WebSocket.
* Invia la transizione $(S_t, A_t, R_t, S_{t+1}, D_t)$ a una coda/canale thread-safe (`Thread-safe Channel`).
2. **Thread 2: Background Trainer Thread (SAC-GRU Engine)**
* Preleva le transizioni dal canale e le accumula in un **Sequential Replay Buffer**.
* Quando il buffer contiene un numero sufficiente di esperienze, estrae mini-batch di sequenze temporali.
* Esegue l'addestramento in background (Forward/Backward Pass dell'Actor, dei Dual Critic e dell'Alpha autotuning) sfruttando l'accelerazione hardware (GPU/CPU).
* Aggiorna periodicamente i pesi della rete Actor usata dal Thread 1 in modo thread-safe (es. scambio atomico di puntatori o mutua esclusione leggera).
---
## 2. Specifiche dell'Ambiente DRL (POMDP)
### 2.1 Vettore di Stato ($S \in \mathbb{R}^{17}$, Normalizzato in $[-1, 1]$)
* `s[0]`: Posizione X propria ($X / \text{width}$)
* `s[1]`: Posizione Y propria ($Y / \text{height}$)
* `s[2]`: Orientamento scafo ($[-\pi, \pi] / \pi$)
* `s[3]`: Velocità lineare propria ($[-8, 8] / 8$)
* `s[4]`: Orientamento cannone ($[-\pi, \pi] / \pi$)
* `s[5]`: Orientamento radar ($[-\pi, \pi] / \pi$)
* `s[6]`: Temperatura del cannone ($[0, 3] / 3$)
* `s[7]`: Energia propria ($[0, 100] / 100$)
* `s[8]`: Distanza dal muro NORD ($(\text{height} - Y) / \text{height}$)
* `s[9]`: Distanza dal muro SUD ($Y / \text{height}$)
* `s[10]`: Distanza dal muro EST ($(\text{width} - X) / \text{width}$)
* `s[11]`: Distanza dal muro OVEST ($X / \text{width}$)
* `s[12]`: Ultima distanza rilevata del nemico ($[0, \text{max\_dist}] / \text{max\_dist}$)
* `s[13]`: Angolo relativo (bearing) del nemico ($[-\pi, \pi] / \pi$)
* `s[14]`: Orientamento del nemico ($[-\pi, \pi] / \pi$)
* `s[15]`: Velocità del nemico ($[-8, 8] / 8$)
* `s[16]`: Energia residua del nemico ($[0, 100] / 100$)
### 2.2 Vettore delle Azioni Continuo ($A \in \mathbb{R}^4$, Output $[-1, 1]$)
* `a[0]`: Rotazione scafo $\rightarrow$ Mappato su $[-10^\circ, +10^\circ]$ per tick.
* `a[1]`: Traslazione $\rightarrow$ Mappato su $[-8, +8]$ px/tick.
* `a[2]`: Rotazione cannone $\rightarrow$ Mappato su $[-20^\circ, +20^\circ]$ per tick.
* `a[3]`: Potenza di sparo $\rightarrow$ $\text{ReLU}(a_3) \times 3.0$ (Spara solo se $> 0.1$).
### 2.3 Reward Function
$$R_t = R_{\text{danno\_inflitto}} - R_{\text{danno\_subito}} + R_{\text{vittoria/sconfitta}} - R_{\text{muri}} - R_{\text{sparo\_vuoto}}$$
* Danno inflitto: $+ (4p + 2(p - 1))$ con $p \le 3$.
* Danno subito: $- (4p_{\text{nemico}} + 2(p_{\text{nemico}} - 1))$.
* Muri: $-5.0$ per tick di impatto.
* Sparo a vuoto: $-0.1 \times p$.
* Vittoria/Sconfitta: $+20.0$ / $-10.0$.
---
## 3. Modelli Neurali e Meccanismi di Addestramento
Tutte le reti devono integrare una cella ricorsiva **GRU (Gated Recurrent Unit)** per gestire la parziale osservabilità dell'arena (radar in rotazione):
* **Recurrent Actor Network $\pi_\phi(a_t | s_{:t}, h_{t-1})$:**
* Feature Extractor: Linear/Dense ($17 \rightarrow 128$) + Attivazione
* Memory: GRU Layer (Input: $128$, Hidden: $128$)
* Heads: Linear/Dense ($128 \rightarrow 64$) $\rightarrow$ Outputs: $\mu \in \mathbb{R}^4$ e $\log \sigma \in \mathbb{R}^4$ (Clamped $[-20, 2]$)
* Sampling: Reparameterization Trick con squashing $\tanh$.
* **Recurrent Dual-Critic Networks $Q_{\theta_1, \theta_2}(s_{:t}, a_{:t}, h_{t-1})$:**
* Fusion: Concatenazione di Stato e Azione ($17 + 4 = 21$)
* Feature Extractor: Linear/Dense ($21 \rightarrow 128$) + Attivazione
* Memory: GRU Layer (Input: $128$, Hidden: $128$)
* Q-Head: Linear/Dense ($128 \rightarrow 64$) $\rightarrow$ Output Q-Value scalare.
* **Sequential Replay Buffer & Burn-in Strategy:**
* Campionamento di sequenze temporali contigue di lunghezza $L = 24$.
* **Burn-in ($L_{\text{burn}} = 8$ step):** I primi 8 step vengono usati unicamente per aggiornare lo stato nascosto $h$ della GRU, senza calcolo di loss o backpropagation.
* **Training ($L_{\text{train}} = 16$ step):** I successivi 16 step calcolano le loss dell'Actor, dei Critic e l'autotuning del parametro di temperatura $\alpha$ (con target $H_{\text{target}} = -4.0$).
---
## 4. Requisiti di Strutturazione del Codice
Fornisci il codice sorgente completo, modulare e pronto all'uso articolato nelle seguenti componenti:
1. **Model Definitions:** Classi/Strutture per Actor, Critic e Target Networks basate su GRU.
2. **Sequential Replay Buffer:** Struttura dati thread-safe con supporto per estrazione di sequenze e gestione Burn-in.
3. **SAC-GRU Trainer Engine:** Algoritmo di aggiornamento, calcolo loss, gradient clipping ($1.0$) e soft update ($\tau = 0.005$).
SAC_LSTM_Bot_garage/docs/campaign_dashboard.svg
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<rect width="1400" height="1850" fill="white"/>
<text x="700" y="32" text-anchor="middle" font-size="21" font-weight="bold">SAC-LSTM campaign dashboard - live run (current only)</text>
<text x="700" y="56" text-anchor="middle" font-size="12" fill="#555">generated 2026-08-24 12:15:51 - auto-reloads every 60 s (open this file in Chrome)</text>
<text x="70" y="100" font-size="15" font-weight="bold">Test matches - win % vs opponents</text>
<text x="70" y="115" font-size="11" fill="#555">raw dots = single test matches, thick = rolling-mean-10</text>
<text x="70" y="133" font-size="12" font-style="italic" fill="#555">What: How often the bot wins against each opponent in test battles.</text>
<text x="70" y="149" font-size="12" font-style="italic" fill="#555">Axes: X = training progress (match number); Y = win rate, 0-100%.</text>
<text x="70" y="165" font-size="12" font-style="italic" fill="#555">Better: higher is better.</text>
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<text x="383" y="817" text-anchor="middle" font-size="12">test match number (each opponent)</text>
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<text x="116" y="876" font-size="12">Target - 2 evals</text>
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<text x="747" y="115" font-size="11" fill="#555">training_metrics.jsonl - big early spikes are normal</text>
<text x="747" y="133" font-size="12" font-style="italic" fill="#555">What: How well the brain is learning: losses should fall; alpha sets explore/exploit.</text>
<text x="747" y="149" font-size="12" font-style="italic" fill="#555">Axes: X = training progress; left Y (log) = losses; right Y (linear) = alpha.</text>
<text x="747" y="165" font-size="12" font-style="italic" fill="#555">Better: lower losses = better; alpha falls over time as the bot gets confident.</text>
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<text x="16" y="484" text-anchor="middle" font-size="12" transform="rotate(-90 16 484)">loss (log)</text>
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<text x="793" y="858" font-size="12">|actor_loss|</text>
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<text x="70" y="989" font-size="12" font-style="italic" fill="#555">Axes: X = training progress (10-game chunks); Y = games per hour.</text>
<text x="70" y="1005" font-size="12" font-style="italic" fill="#555">Better: higher = faster learning; a steady line beats a spiky one.</text>
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<text x="1061" y="1527" text-anchor="middle" font-size="12">eval cycle number</text>
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<text x="793" y="1586" font-size="12">Target - 2 evals</text>
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<text x="793" y="1604" font-size="12">combined max</text>
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<text x="16" y="1666" font-size="15" font-weight="bold">Reading the signs</text>
<text x="1384" y="1665" text-anchor="end" font-size="11" fill="#666">Regenerate anytime: python3 tools/plot_progress.py</text>
<text x="16" y="1688" font-size="13" font-weight="bold">Healthy patterns ✅</text>
<text x="16" y="1706" font-size="12">• Both critic and actor losses trending down over time</text>
<text x="16" y="1720" font-size="12">• Alpha decaying slowly from ~1.0,</text>
<text x="16" y="1734" font-size="12"> then plateauing — this is normal</text>
<text x="16" y="1748" font-size="12">• Win rates appearing and increasing in the eval panel</text>
<text x="16" y="1762" font-size="12">• Max scores rising in the max-score panel</text>
<text x="16" y="1776" font-size="12">• Alpha plateauing is NOT a problem — it means</text>
<text x="16" y="1790" font-size="12"> exploration level is stable</text>
<text x="500" y="1688" font-size="13" font-weight="bold">Warning signs ⚠️</text>
<text x="500" y="1706" font-size="12">• Losses exploding (suddenly jumping to</text>
<text x="500" y="1720" font-size="12"> millions or billions)</text>
<text x="500" y="1734" font-size="12">• Win rates staying at 0% for a long time</text>
<text x="500" y="1748" font-size="12"> after the first ~20 eval cycles</text>
<text x="500" y="1762" font-size="12">• Alpha reaching 0 — bot stops exploring entirely</text>
<text x="500" y="1776" font-size="12"> (gets stuck)</text>
<text x="500" y="1790" font-size="12">• Max scores flatlining (no improvement</text>
<text x="500" y="1804" font-size="12"> over many eval cycles)</text>
<text x="500" y="1818" font-size="12">• Any single loss value above 1e6</text>
<text x="985" y="1688" font-size="13" font-weight="bold">What each metric means (brief)</text>
<text x="985" y="1706" font-size="12">• Critic loss: how wrong the bot's value</text>
<text x="985" y="1720" font-size="12"> estimates are — should go down</text>
<text x="985" y="1734" font-size="12">• Actor loss: how well the bot's action policy</text>
<text x="985" y="1748" font-size="12"> is doing — should go down overall</text>
<text x="985" y="1762" font-size="12"> (some bumps are normal)</text>
<text x="985" y="1776" font-size="12">• Alpha: exploration-exploitation tradeoff — starts</text>
<text x="985" y="1790" font-size="12"> high, settles at a positive value (NOT zero)</text>
<text x="985" y="1804" font-size="12">• Max score: best score achieved per eval cycle —</text>
<text x="985" y="1818" font-size="12"> rising trend = learning</text>
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## The story so far, in simple words
This project trains a robot tank. It plays many fights against other tanks.
After each fight it changes itself a little. It keeps the changes that helped it win.
Night 1 (run 1) finished without problems. It ran for 14 hours alone. It never crashed.
It beat an old copy of itself most of the time. It beat Crazy about half the time.
Three things went badly. First, learning was not stable. Good skill appeared, then disappeared again.
Second, the saved "best" version came from one lucky perfect score. It was not really its best.
Third, the bot learned to hide and survive. It almost never shot back.
The human approved five fixes. All five were put into the code.
Run 2 used these fixes. Its error numbers grew far too big. Learning broke.
We made one speed number smaller. This number sets how fast one part learns.
Then we dropped the broken progress and started clean. This is run 3. It is running now.
Next we watch run 3. One of three doors will open.
Door 1: it stays steady. We let it run to the end.
Door 2: the numbers grow too big again. We turn the next speed number down.
Door 3: it stays steady but still fights badly. We teach aiming as a separate, direct lesson.
Updated: 2026-08-23 — this section is refreshed at every major step.
## Small dictionary
- **training**: the time when the bot plays fights and changes itself to improve. It learns only during training.
- **battle**: one group of fights against one opponent. The bot restarts between groups.
- **round**: one single fight. Win it by destroying the enemy tank or outliving it.
- **chunk**: one work block: a battle of up to 10 rounds, then some learning from it.
- **eval (test match)**: a test match. The bot does not learn during these. We use them only to measure.
- **win rate**: how many test matches were won, as a percent. 8 wins in 10 matches = 80%.
- **checkpoint**: a saved copy of the bot's brain (a zip file). Written every few learning steps.
- **"best" checkpoint**: the saved copy we currently call best. Run 1 picked one from a lucky score, hence the quotes.
- **replay buffer**: the bot's memory of past moments: what it saw, did, and received. Learning picks old moments from it.
- **loss (critic/actor)**: a number saying how wrong the bot's inner guesses are. Lower usually means better. Losses growing huge mean trouble.
- **alpha**: a dial setting how much the bot tries new moves instead of repeating known good ones.
- **MA / composite score**: MA is the average of the last few win rates; it smooths luck. Composite is the average of MAs across all test opponents.
- **twin (SacTwin)**: a frozen copy of our own bot, used as a practice partner. Beating it proves real improvement.
- **lever**: one numbered change we prepared, waiting for approval. There are levers 1 to 5.
- **watchman**: a helper who checks the running training at set times and stops it if something breaks.
---
# Campaign Notebook — campaign-v1 (SAC_LSTM_Bot)
Overnight training campaign on branch `research/goto-controller`.
Companion tickets: config+launch = **#56**, morning verdict = **#57**, map = **#53**.
Monitoring contract: observability inventory in **#55 comment 461** (9 signals, thresholds, four-way discrimination).
## Locked config (campaign-v1)
Launch command (tmux session `sac_campaign`, stdout teed to `campaign_stdout.log`):
```bash
SAC_OPPONENTS='Corners:3,Crazy:2,RamFire:1,Target:1,SacTwin:1' \
SAC_EVAL_OPPONENT=Corners \
SAC_TOTAL_ROUNDS=25000 \
SAC_CHUNK_SIZE=10 \
SAC_EVAL_INTERVAL=2 \
SAC_EVAL_ROUNDS=10 \
SAC_MAX_CRASHES=5 \
SACLSTM_HIDDEN_SIZE=256 \
SACLSTM_BATCH_SIZE=16 \
SACLSTM_UTD_RATIO=1 \
SACLSTM_SAVE_INTERVAL=5 \
./sac_train.sh 2>&1 | tee -a campaign_stdout.log
```
| Knob | Value | Source / rationale |
|------|-------|--------------------|
| `SAC_OPPONENTS` | `Corners:3,Crazy:2,RamFire:1,Target:1,SacTwin:1` | Working recommendation kept. Twin pinned at **1 not 2**: #54 showed mirror battles end early ⇒ fewer transitions per chunk; weight 2 would starve the replay buffer. |
| `SAC_EVAL_OPPONENT` | `Corners` | Pinned explicitly (= first pool entry default, #54 note) — removes reorder footgun. |
| `SAC_TOTAL_ROUNDS` | `25000` | Sized so the **wall-clock ceiling binds first**: measured throughput ~3400 rounds/h early (drops as battles lengthen) ⇒ 2000 would have exhausted in ~1 h. |
| `SAC_CHUNK_SIZE` | `10` | Harness default. |
| `SAC_EVAL_INTERVAL` | `2` | Harness default — eval every ~20 rounds. |
| `SAC_EVAL_ROUNDS` | `10` | Harness default. |
| `SAC_MAX_CRASHES` | `5` | Harness self-abort; monitor intervenes earlier at ≥3 consecutive crashes (#55). |
| `SACLSTM_HIDDEN_SIZE` | `256` | Module default (`network.nim`); real capacity vs #49 smoke's 32; under `MaxHidden`=512 cap. |
| `SACLSTM_BATCH_SIZE` | `16` | Module default (`integration.nim`). |
| `SACLSTM_UTD_RATIO` | `1` | Module default. |
| `SACLSTM_SAVE_INTERVAL` | `5` | **Deviation** from default 500 and from #54's "10–20": at hidden 256 a gradient step takes ~1 s and a chunk process fits only ~10–18 steps (see incident below) — interval must sit **inside the per-process step budget**. 5 ⇒ checkpoint every ~5–10 s of active training; IO trivial (10.5 MB zip, atomic replace). |
| *(not pinned)* | module defaults | `LR_ACTOR/LR_CRITIC/LR_ALPHA=3e-4`, `GAMMA=0.99`, `TAU=0.005`, `TARGET_ENTROPY=-4.0`, `BUFFER_CAPACITY=500000`, `BURN_IN=8`, `TRAIN_WINDOW=16`. |
**Budget**: generous wall-clock **ceiling, not a deadline** — originally **T+12 h** from launch 00:21:55 CEST 2026-08-22 ⇒ 12:22 CEST (epoch 1787394115); **extended 2026-08-22 ~07:35 by orchestrator decision on human mandate** ("no deadlines — let the 25000-round budget complete", ~14:35 projected) ⇒ ceiling now **16:30 CEST 2026-08-22 (epoch 1787409000)**, enforced by a hard user-systemd net unit `sac-ceiling-net` (sleeps to the epoch, then kills the tmux session and any straggler harness processes). While HEALTHY per #55 discrimination rules the run continues; a monitor kills the tmux session at the ceiling or on an intervene threshold.
**Fresh start**: pre-campaign `weights/` held #49-smoke 32-hidden checkpoints, incompatible with hidden=256. Archived to `weights_smoke49_backup/`; campaign baseline re-established by probe battles vs Corners (random-init hidden-256 checkpoint, `best_score.txt` reset then re-raised to 20 by a genuine eval). Twin regenerated via `./make_twin.sh` from that baseline (md5 `61521cff…` verified seed).
## Phase log
Every entry below starts with a plain-language first sentence. Technical detail follows for those who want it.
- **2026-08-21 23:22** — Claim posted on #56 (comment 471). Config locked, notebook committed (`2f49cb2`).
- **2026-08-21 23:27** — Smoke weights archived; release build; bootstrap + probe battles vs Corners established a hidden-256 baseline checkpoint (`sac_latest.zip`, 10.5 MB) and twin seed.
- **2026-08-21 23:32** — Twin regenerated (md5-verified). **Launch attempt 1** (SAVE_INTERVAL=20, TOTAL_ROUNDS=2000): ran 16+ chunks, evals every 2 chunks — but **zero checkpoints persisted** (see incident). Killed 23:48.
- **2026-08-21 23:52–00:10** — Diagnosis (see incident): interval=1 fired, interval=2/20 never; instrumentation + /proc thread forensics ⇒ per-process step budget ~10–18 at ~1 s/step; save check ran only between drain-burst passes.
- **2026-08-22 00:12** — Fix: save check moved inside the gradient-step loop, committed `2653671`. Validated: interval=5 save fired ~12 s into a battle.
- **2026-08-22 00:21:55** — **Launch (final)**: tmux `sac_campaign`, config above. First campaign save on disk at t+54 s; eval #1 on cadence.
- **2026-08-22 00:30** — HEALTHY checklist passed (see below).
- **2026-08-22 03:45** — Watch shift 1 (00:34–03:35): liveness flawless (10/10 HEALTHY, zero banners, zip ≤30 s). Learning signal: steady-state eval vs Corners 0–5% with two isolated 10/10 spikes (~01:15) → capability emerged, then lost. Eval-regression intervene threshold fired per #55; intervention DEFERRED to Campaign verdict (#57) — rationale: n=2 evidence, no loss metrics, buffer-loss on restart, run completes ~08:05 anyway. Milestone issue: #58 "Campaign-v1 watch: eval-regression threshold fired — intervention deferred to verdict".
- **2026-08-22 ~07:15** — Morning audit: policy demonstrably learning off-benchmark (SacTwin 73→100%, Crazy 26→56%) while Corners eval stays ~0–9% with 3 transient 10/10s; wall-clock ceiling extended to let the 25k complete (~14:35 projected); instability-vs-plateau question left to the curve.
- **2026-08-22 ~07:50 — score:60 anatomy**: Tank Royale survival(50)+last-survivor(10) awarded when opponent dies while we survive; exactly-60 ⇒ zero damage dealt by us that round (opponent self-destructed via wasted shots + 0.1/turn inactivity drain). 3,878 rounds (19%); modal vs SacTwin; vs Corners 986 damageless outlives vs 242 true wins; combined with 43% of rounds being score:0, texture = survivor-not-fighter against walls. RL rewards are event-driven (rewards module), so behavioral evidence, not reward poisoning. Feeds #57 levers: aggression shaping / specialist-vs-generalist.
- **2026-08-22 ~08:05 — `.part` debris forensics + sweep**: 99 `*.zip.tmp.*.part` (483 MB) are NOT weights.nim debris (that proc uses fixed `.tmp` + finally-cleanup, working); naming matches an external write-temp→rename copier killed mid-write, bursts correlating with kill events; possible culprit: a folder-sync client fighting a file that changes every ~20 s (**human asked to confirm**). Swept with `-mmin +10` age guard (protects in-flight writes); 483 MB freed, real zips untouched — note 2 fresh `.part` reappeared minutes later, copier still active.
- **2026-08-22 ~08:00 — ceiling defused**: the 12:22 'self-kill' was notebook prose instructing watchmen — never an OS mechanism; rewritten to 16:30 CEST AND armed a real systemd --user net `sac-ceiling-net` firing 16:30:00 (epoch 1787409000); training uninterrupted (counter +99/147 s verified); annotated #56 comment 487.
- **2026-08-22 ~14:28 — CAMPAIGN ENDED NATURALLY**: banner `>>> training complete: 2500 chunks` after **14 h 07 m** (00:21:55 → ~14:28 CEST); round_counter **38912**; **zero crash banners across the whole run**; the `sac-ceiling-net` backstop never fired — cancelled unneeded. Budget note: the harness loop is **chunk-based** — `SAC_TOTAL_ROUNDS=25000` ÷ `CHUNK_SIZE=10` ⇒ **2500 chunk battles** of ≤10 rounds each; the "25k-rounds" label was a misnomer (the counter also accrues 1287×10 eval rounds and rerun chunks). Final eval vs Corners: 10%.
- **2026-08-22 ~14:50 — VERDICT posted (→ #57)**: **RETUNE BEFORE SCALING.** Ops layer PROVEN (14 h autonomous, zero crashes, self-healing restarts, natural completion — the harness scales); learning REAL BUT NARROW (within-opponent gains genuine — SacTwin 90.7%, Crazy 26→48% — but specialist-not-generalist, walls untouched; 12 eval spikes ≥8/10 incl. 5×10/10, none retained); benchmark pathology: Corners-only deterministic eval + single-max best gating froze `sac_best.zip` at 01:01:57 on a fluke 10/10. Five code-level levers staged **awaiting human sign-off**; v2 NOT launched. Full rationale: Results below + #57 resolution comment.
- **2026-08-22 ~14:50 — HYGIENE (campaign over, no live writers — safe)**: `sac-ceiling-net` stopped + `reset-failed` (backstop obsolete); `.part` corpse sweep **48 → 0** (no age guard needed — nothing writes anymore); future-run guard added to `sac_train.sh`: startup `rm -f "$WEIGHTS_DIR"/sac_latest.zip.tmp.*.part "$WEIGHTS_DIR"/sac_latest.zip.tmp` so a SIGKILLed run's libzip modify-path corpses can't accumulate again.
## Decision-issue index
| Issue | What it decided |
|-------|-----------------|
| #37–#48 | Bot built: skeleton, state, actions, rewards, LSTM network, weights, SAC+LSTM training, integration. |
| #49 | Training harness + smoke run (toy hyperparams: hidden 32). |
| #54 | Mirror-twin sparring partner; SAVE_INTERVAL persistence rule; eval opponent = first pool entry. |
| #55 | 9-signal observability inventory; CRASHED/STALLED/SLOW-LEARNER/HEALTHY discriminators; monitor thresholds. |
| #56 | This campaign: locked config + launch + the save-check fix (`2653671`). |
| #57 | Morning verdict — consumes this notebook + logs. |
## Incidents & checks
### Incident 1 — zero checkpoint persistence at production sizes (launch blockers, fixed)
**Symptom**: campaign ran 26+ chunk processes across two attempts without a single `sac_latest.zip` update, while rounds/evals flowed normally. #54's rule ("keep `SACLSTM_SAVE_INTERVAL` well below per-chunk gradient-step counts, 10–20 fired in smokes") silently broke at hidden 256.
**Diagnosis chain** (all reproducible):
1. Interval=1 saved within seconds; interval=2 and 20 never saved — through the *same* harness ⇒ not env propagation.
2. Temporary step instrumentation (bot stderr via a one-line `SAC_LSTM_Bot.sh` redirect — the vendored runner swallows bot stderr, #55 gap S9-adjacent): steps cost **~1.06 s each**; a drain burst queued 53 steps; logging stopped mid-pass while rounds kept completing.
3. `/proc/<pid>/task` sampling: training thread alive and RUNNING (~13 s CPU per ~40 s process) — not deadlocked, just slow ⇒ **per-process step budget ≈ 10–18 steps**.
4. The save check lived *between* drain-burst passes; with bursts queueing minutes of steps, `stepCount` never reached `nextSave` before process teardown. Smoke runs masked this: hidden 32 steps were sub-millisecond, so hundreds of steps fit per chunk.
**Fix** (commit `2653671`): save check relocated **inside** the step loop (checked every gradient step; `packFull`+`trySend` unchanged). Validated: interval=5 save fires ~12 s into a battle; campaign save fired 54 s after launch.
**Config consequences**: `SACLSTM_SAVE_INTERVAL=5` (inside the per-process budget; #54's 10–20 was derived at smoke speeds). `SAC_TOTAL_ROUNDS=25000` (throughput measured ~3400 rounds/h, so 2000 was a 1-hour budget, not an overnight one). OMP_NUM_THREADS=1 tested and **not** needed (hang was step-budget exhaustion, not OpenMP).
### Launch health check (t+9 min, 00:30:16) — **HEALTHY** per #55 checklist
| Signal | Reading | Verdict |
|--------|---------|---------|
| S1 harness stdout | teed to `campaign_stdout.log`; **0** `crash`/`aborted` banners | ✓ |
| S4 round_counter | 1890, +460 in 9 min (~51 rounds/min) | ✓ advancing |
| S6 sac_latest.zip mtime | **6 s old**; first save at t+54 s | ✓ fresh |
| S2 training_log.jsonl | 1492 lines, growing; last ticks=668, plausible | ✓ |
| S3 eval_log.jsonl | age 3 s (atomic replace); eval every 2 chunks | ✓ on cadence |
| S5 best_score | 20 (from a genuine campaign-1 eval; non-decreasing) | ✓ |
| Sampling | 112 chunks: Corners 41 / Crazy 25 / RamFire 15 / SacTwin 16 / Target 15 ≈ weights 3:2:1:1:1 | ✓ plausible |
| Disk | 418 GB free | ✓ |
Early evals 0% vs Corners — expected for a near-random policy minutes in; SLOW-LEARNER watch rule (flat ≥5 evals = watch) applies, never intervene.
## Check-in procedure (for monitor sessions)
```bash
tmux capture-pane -p -t sac_campaign | tail -5 # S1: banners, crashes
cat ~/Projects/SirRoboGarage/SAC_LSTM_Bot/weights/round_counter.txt
stat -c '%Y' ~/Projects/SirRoboGarage/SAC_LSTM_Bot/weights/sac_latest.zip # age <~600s = training alive
tail -1 ~/Projects/SirRoboGarage/SAC_LSTM_Bot/training_log.jsonl
tail -3 ~/Projects/SirRoboGarage/SAC_LSTM_Bot/campaign_stdout.log # eval results / new best
cat ~/Projects/SirRoboGarage/SAC_LSTM_Bot/weights/best_score.txt
```
Intervene per #55 thresholds: ≥3 consecutive `crash #N` banners; ΔS4=0 over ≥15 min; zip mtime >10 min stale while S4 advances (STALLED); disk <1 GB. At the **ceiling (16:30 CEST Aug 22, epoch 1787409000 — extended from 12:22 per human mandate)**: `tmux kill-session -t sac_campaign` if still running (the hard net unit `sac-ceiling-net` fires at the same epoch regardless of monitors) — final state is in `weights/`, logs, and this notebook.
## Results (campaign-v1 — filled by #57)
**Run**: 2500/2500 chunks · **14 h 07 m** autonomous (00:21:55 → ~14:28 CEST 2026-08-22) · round_counter 38912 · **zero crashes** · graceful banner `>>> training complete: 2500 chunks` · systemd net never fired. Budget was chunk-based (see phase log) — the "25k-rounds" label was a misnomer.
**Per-opponent training win rates** (run-3 slice of `training_log.jsonl`):
| Opponent | Win rate | Record | Note |
|----------|----------|--------|------|
| SacTwin | **90.7%** | 2693/2970 | vs frozen past-self — genuine self-play gain |
| Crazy | 48.3% | 2965/6140 | doubled from 26% early-run |
| Target | 9.6% | — | static, barely moved |
| Corners | 7.2% | — | walls untouched |
| RamFire | **0%** | 0/2920 | mirrors the PPO-era ladder — ram-class needs dedicated pressure |
**Eval vs Corners (pinned benchmark)**: 1287 evals · overall mean **7.7%** · histogram headline: `0/10 = 867 (67%)`, spikes ≥8/10 = **12** (incl. **5× perfect 10/10**) · final eval 10%. Stdout log carries no timestamps; timing reconstructed from file mtimes.
**Best-zip paradox**: `sac_best.zip` frozen since **01:01:57** — a single lucky 10/10 at ~round 3.5k wrote `best_score=100`, and no later eval could outrank a perfect score (even genuine ~50%-winrate stretches elsewhere). Best checkpoint = lottery ticket, decoupled from the steady-state policy (which sat at 0–10% vs Corners).
**Verdict: RETUNE BEFORE SCALING** — full rationale in #57 resolution comment. Five code-level levers staged for human sign-off: (1) eval rotation across pool + moving-average best gating; (2) reward shaping toward damage/aggression incl. anti-ram signal; (3) training-loss/step metrics logged from the training thread (#55 gap #1); (4) gate `sendTrainingMsg` off in eval mode (#55 gap #3); (5) optional stability knobs (lower LR / entropy coeff) once loss curves exist.
---
# Campaign Notebook — campaign-v2 (SAC_LSTM_Bot)
## Locked config (campaign-v2)
Launched verbatim from orchestrator mandate (umbrella ticket #59, all five levers approved & implemented in `a07e530` + `6fc01eb`):
```bash
cd /home/davide/Projects/SirRoboGarage/SAC_LSTM_Bot && \
SAC_OPPONENTS='Corners:3,Crazy:2,RamFire:2,Target:1,SacTwin:1' \
SAC_EVAL_OPPONENTS='Corners,Crazy,Target' \
SAC_EVAL_INTERVAL=2 SAC_EVAL_ROUNDS=10 \
SAC_TOTAL_ROUNDS=25000 SAC_CHUNK_SIZE=10 SAC_MAX_CRASHES=5 \
SACLSTM_HIDDEN_SIZE=256 SACLSTM_BATCH_SIZE=16 SACLSTM_SAVE_INTERVAL=5 \
./sac_train.sh 2>&1 | tee -a campaign_v2_stdout.log
```
| Knob | Value | Rationale |
|------|-------|-----------|
| `SAC_OPPONENTS` | Corners:3, Crazy:2, **RamFire:2**, Target:1, SacTwin:1 | RamFire bumped 1→2 vs v1: anti-ram shaping (lever 2, `6fc01eb`) needs exposure to fire; without samples there is no gradient signal against ram-class |
| `SAC_EVAL_OPPONENTS` | Corners,Crazy,Target | Lever-1 rotation set (default); composite = mean of per-opponent MA-5 win rates |
| `SAC_EVAL_INTERVAL/ROUNDS` | 2 / 10 | Unchanged from v1 cadence |
| `SAC_TOTAL_ROUNDS/CHUNK_SIZE/MAX_CRASHES` | 25000 / 10 / 5 | Same budget semantics as v1 (chunk-based) |
| `SACLSTM_HIDDEN_SIZE/BATCH_SIZE/SAVE_INTERVAL` | 256 / 16 / 5 | Architecture + throughput knobs carried over; LR/entropy defaults untouched = **lever-5 conditional posture** (activation decided by loss-curve evidence, not upfront) |
## Fresh start & archive
v1 state archived intact (notebook references preserved) into `SAC_LSTM_Bot/weights_v1_archive/`: `sac_latest.zip`, `sac_best.zip`, `best_score.txt`, `round_counter.txt`, `training_log.jsonl`, `eval_log.jsonl`, `campaign_stdout.log`, plus the lever-3 smoke leftover `training_metrics.jsonl` (from `src/SAC_LSTM_Bot/`, moved so v2 loss curves start clean for lever-5 reading). Main `weights/` verified empty afterwards ⇒ main bot takes the genuine random-init path (`loadOrInitFull` → `randomFull()`).
**Twin reseed**: `make_twin.sh` requires a seed zip, but the fresh-start baseline has none. Generated a fresh random-init checkpoint (hidden=256, alpha=1.0 matching `logAlpha=0`) via a throwaway Nim script against `network.nim`/`weights.nim`, seeded it as `sac_best.zip` transiently, ran `./make_twin.sh`, removed the transient copy. Verified twin dir got byte-identical fresh zips (`cmp` OK; NOT v1 zips) + `round_counter.txt=0`. No script changes needed.
## Safety net
`systemd-run --user --unit=sac-ceiling-net-v2` armed at launch: sleeps 72000 s then `tmux kill-session -t sac_campaign_v2; sleep 5; pkill -f sac_train.sh`. Unit active at 19:59:40 CEST 2026-08-22, fires **15:59:40 CEST 2026-08-23** (epoch 1787493580).
## Launch & health evidence (first ~45 min)
Launched 20:00:19 CEST 2026-08-22 (epoch 1787421619), tmux session `sac_campaign_v2`.
| Check | Evidence |
|-------|----------|
| Round counter advances | `round_counter.txt` 95→100→465 across polls; RunTraining `Counter check passed: N == N` every chunk |
| Metrics JSONL with scalars | `training_metrics.jsonl` growing (20 lines @ t+45m): full `{epoch, steps, buffer_size, drained, grad_steps, critic_loss, actor_loss, alpha_loss, alpha}` per line |
| Eval rotation cycles ≥2 | All 3 opponents EVERY cycle: Corners→Crazy→Target ×4+ cycles in `eval_log.jsonl` (10 games each per cycle) |
| MA files written | `weights/ma_history_{Corners,Crazy,Target}.txt` created at first cycle, appended since |
| Best-gate on composite only | First write exactly when composite 0.0000 > −1 (missing-file default); later 0% cycles correctly did NOT rewrite (strict improvement enforced) |
| No transitions during eval windows | Lever-4 gate active by construction (`sendTrainingMsg` drops all msgs under `SACLSTM_EVAL_MODE=1`, unit-tested); metrics epochs cluster at chunk boundaries |
| Zero crash banners | `grep -c 'crash #'` = 0 through 20 chunks |
| Sampling distribution plausible | 20 chunks: Corners 10, RamFire 5, Crazy 3, SacTwin 2, Target 0 — within small-n noise of weights (3/2/2/1/1)/9; RamFire already sampled (exposure goal met) |
Twin liveness: own `round_counter.txt` advancing, own `sac_latest.zip` updating during SacTwin chunks, own metrics file separate from the main bot's.
### Watch items (not blockers)
1. **Training-throughput signature**: metrics lines consistently show `steps=1, buffer_size=24 (= burnIn 8 + trainWindow 16, i.e. exact canSample threshold), drained=1` — one gradient step per pass at threshold-crossing moments rather than large drain bursts. Mechanism unexplained by static code read (per-tick sends should yield bigger bursts); v1 learned to its score-60 state under the same integration code without instrumentation, so learning is not obviously broken — but effective grad-steps/hour is THE number to check at first review. This is precisely what lever-3 instrumentation exists to surface.
2. **Early critic-loss spikes**: two `1.56e16` outliers (t+6:16, t+12:04) amid otherwise sane values (~8–35) — same class as the random-init artifact flagged in #59 phase-2 notes (~1e12 there); expect decay. If persistent past early chunks, feeds the lever-5 decision.
3. **`.part` corpses**: three `sac_latest.zip.tmp.*.part` files accumulated mid-run (libzip interrupted-write artifact, #57 forensics); harmless — atomic renames keep the main zips valid, startup sweep clears them next restart.
## ~21:30 — v2 attempt-1 checkpoint finding
Attempt-1's rolling zip was dead ~40 min at birth: `SACLSTM_SAVE_INTERVAL` counts **gradient steps**, and each training pass inside the short battle processes carries exactly **1 step** (the `steps=1, drained=1` signature of watch item 1) ⇒ interval-5 never reached its trigger within a process lifetime — no `sac_latest.zip` roll ever fired despite healthy training. Same class as v1's 23:32 incident, resurfacing through a different seam (per-process step budget vs per-pass step count).
**Fix (env-level, no rebuild)**: `SACLSTM_SAVE_INTERVAL=1`, restart 21:09:30 CEST. Saves verified flowing: 21:09 / 21:13 / 21:16 (and still flowing at audit time — `sac_latest.zip` mtime 21:22:28, `sac_best.zip` 21:12:50). The locked-config table above keeps the original attempt-1 launch line for the record; live relaunch differs only in this knob.
## ~21:30 — twin-freeze contract corrected
`make_twin.sh` twin launcher now exports `SACLSTM_EVAL_MODE=1` (commit `19f34ab`). Without lever-4's gate on the twin side, the **v1 SacTwin had been TRAINING throughout**, not frozen — every mirror battle updated the twin's own weights. Consequence: v1's headline "**90.7% vs twin**" is retroactively an **arms-race win rate** (both policies co-evolving), not a fixed-benchmark score, and the #57 verdict phrasing implying a frozen sparring partner is corrected by this entry. No numbers change; the story does.
## ~21:30 — net extended + pacing decision
Slowdown attribution complete: **89% of wall clock = eval rotation by design** (`SAC_EVAL_INTERVAL=2` × 3 opponents × 10 rounds each); trainer exonerated at **~500 ticks/s**. Decision recorded in issue **#60**.
Ceiling net re-armed to match the extended budget: fires **Thu 2026-08-27 07:59:40 CEST (epoch 1787810380)** — supersedes the 2026-08-23 15:59:40 fire noted under Safety net. Attempt-1 artifacts preserved in `/tmp/v2_attempt1_backup/`.
# Campaign v2 — attempt-3 (2026-08-23)
## ~06:45 — LEVER-5 TRIGGERED (watchman shift 2, 02:11–06:22 CEST)
Evidence-gated activation of the lever-5 conditional posture (`SACLSTM_LR_CRITIC`), human signed off. Trigger evidence:
| Signal | Observation |
|--------|-------------|
| actor_loss | \|34M\| → \|106M\| monotone (+15M/h), **no plateau** |
| critic_loss | spikes >1e12 in **100%** of NEW metric records; campaign share 80.2% |
| Signature | exact `1.5625e16` recurring (= float32 saturation neighborhood) |
| alpha | decayed 0.951 → 0.711 (entropy collapse under runaway Q scale) |
| best_score.txt | frozen 00:32 (=83.3333) through 5h50m of composite oscillation 3.3–43.3 |
| Per-opponent MAs | whipsawing (Crazy 30→100→90→0→60→90→10→0) |
## DECISION — LR_CRITIC=1e-4, single knob, fresh start
- **Adjudication**: primary suspect is critic/Q value-scale growth. Actor loss inherits Q magnitude through the policy gradient, so the actor explosion is downstream; alpha decay is a *symptom* (entropy temperature chasing a blown value scale), not a cause. Therefore ONE knob moves: `SACLSTM_LR_CRITIC=1e-4` (critic learns slower → Q estimates stay estimable); LR_ACTOR/LR_ALPHA stay at default 3e-4, TARGET_ENTROPY −4. Multi-knob changes would confound attempt-3's attribution.
- **Fresh start over warm start**: attempt-2's weights carry saturated Q representations; warm-starting them under a new LR would inherit the pathology we're trying to escape (contamination risk). Attempt-2 archived intact, nothing discarded.
## MA-wipe root cause FOUND & FIXED (`167bcc4`)
Watchman anomaly explained: `ma_history_*.txt` held exactly one leading-space value per file (e.g. `[ 10 ]`, `[ 20 ]`, `[ 0 ]` in the attempt-2 backup) instead of the designed 5-value history, degrading the composite best-gate to last-cycle mean — which fully accounts for the composite whipsaw 3.3–43.3.
Root cause: in `eval_checkpoint`, the append line was
```bash
printf '%s\n' "$(cat "$(ma_hist_file "$opp")")" "$wr" | tail -n 5 | tr '\n' ' ' > "$(ma_hist_file "$opp")"
```
Bash sets up the `>` redirect **before** running the command substitution, so `$(cat f)` always read the freshly truncated file ⇒ every cycle wiped history to `" $wr "`. Reproduced standalone on bash 5.3 before touching the script; no other writer exists (grep). Fix hoists the read into its own statement and word-splits it so `tail -n 5` keeps exactly the last MA_WINDOW values. Verified live in attempt-3: two eval cycles produced `[0 0 ]` per opponent (previously impossible).
## Attempt-3 launch (2026-08-23 07:01:28 CEST, epoch 1787461288)
- Attempt-2 stopped cleanly (tmux kill; no stray procs); artifacts (76 items, 336 MB incl. `.part` corpses + MA/best/latest/counter) → `/tmp/v2_attempt2_backup/`; final round_counter **8452**; `weights/` verified empty.
- Twin regenerated via documented transient-seed method: throwaway Nim script (`initSACTrainer(35,4)` + `saveWeights`, hidden=256 env, alpha=1.0) seeded as transient `weights/sac_best.zip` → `./make_twin.sh` → twin zips byte-identical to seed (`cmp` OK), twin counter=0 → transient removed.
- Launch line = locked config verbatim plus the two live deltas:
```bash
SAC_OPPONENTS='Corners:3,Crazy:2,RamFire:2,Target:1,SacTwin:1' \
SAC_EVAL_OPPONENTS='Corners,Crazy,Target' \
SAC_EVAL_INTERVAL=2 SAC_EVAL_ROUNDS=10 \
SAC_TOTAL_ROUNDS=25000 SAC_CHUNK_SIZE=10 SAC_MAX_CRASHES=5 \
SACLSTM_HIDDEN_SIZE=256 SACLSTM_BATCH_SIZE=16 SACLSTM_SAVE_INTERVAL=1 \
SACLSTM_LR_CRITIC=1e-4 \
./sac_train.sh 2>&1 | tee -a campaign_v2_stdout.log
```
- Safety net: existing transient unit `sac-ceiling-net-v2.service` re-verified active; fire epoch start+384587 s = **1787810380 = Thu 2026-08-27 07:59:40 CEST exactly** (delta 0 vs mandate) — kept, not duplicated.
### Health baseline (t+15 m)
| Check | Evidence |
|-------|----------|
| Counter | 33 (t+8m) → **125** (t+15m) |
| Metrics JSONL | flowing (6 lines); baseline line: `critic_loss=23.09, actor_loss=-3.228, alpha_loss=0.0, alpha=0.99970` (random-init magnitudes — attempt-3's divergence curve starts here); t+15m line: critic 84.5, actor −9.99, alpha 0.9937 |
| Saves | `SAVE_INTERVAL=1`: `sac_latest.zip` written from first grad-step onward |
| Eval rotation | full cycle landed: Corners/Crazy/Target ×10 games each in `eval_log.jsonl` |
| MA files | `[0 0 ]` per opponent after two cycles — fix confirmed in vivo |
| Best gate | `best_score.txt=0.0000` written on first composite (0 > −1 default) |
| Crash banners | 0 |
### Progress graphs
One live dashboard: `docs/campaign_dashboard.svg` (current run only — test wins, real-fight wins, losses, alpha, throughput; auto-reloads every 60 s when open in Chrome). Keep it fresh with `tools/watch_dashboard.sh` (regenerates every 60 s), or one-shot `python3 tools/plot_progress.py` (pure stdlib; paths overridable via argv, `--selftest` for sanity check).
## ~10:47 — the dashboard's axes were upside-down since creation
The progress graphs have been lying since they were made: 0% was drawn at the TOP of every panel and the newest games appeared on the LEFT. The cause is a one-line formula bug in `tools/plot_progress.py`: `map_fn` interpolated as `p1 - t*(p1-p0)` instead of `p0 + t*(p1-p0)`, so all five panels plotted `100 - value` on y and reversed time on x. Tick labels were computed by separate (correct) code, which is why the numbers on the axes never matched the ink.
Fix + guard: formula corrected; every call site audited (panels 1/2/5 use `map_fn` for both axes and are fixed by the same line; panels 3/4 already used a correct local x-lambda; no other consumer of `map_fn` exists in the repo). `--selftest` now renders a known rising series through the full build path and fails loudly unless higher value = smaller SVG y and newer data = further right — proven to catch this exact bug when the old formula is re-injected. Dashboard regenerated from live logs.
Honest status while reading the now-correct charts: run 3 is only hours old and winning ~0% — recent evals are 0/10 vs Corners, Crazy and Target alike, and real-fight buckets sit at 0–1 wins per 100 games. Expected for a fresh brain. Night-1's gains were real but were intentionally reset by the stability restart that began attempt-3; the curve starts from zero again here.
SAC_LSTM_Bot_garage/eval_log.jsonl.pre-merge
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{"type":"game","round":1,"ticks":1647,"score":0,"total_score":0,"win":false,"opponent":"Corners"}
{"type":"game","round":2,"ticks":1518,"score":0,"total_score":0,"win":false,"opponent":"Corners"}
{"type":"game","round":3,"ticks":1733,"score":0,"total_score":0,"win":false,"opponent":"Corners"}
{"type":"game","round":4,"ticks":1731,"score":0,"total_score":0,"win":false,"opponent":"Corners"}
{"type":"game","round":5,"ticks":1077,"score":17,"total_score":17,"win":false,"opponent":"Corners"}
{"type":"game","round":6,"ticks":1745,"score":0,"total_score":17,"win":false,"opponent":"Corners"}
{"type":"game","round":7,"ticks":1634,"score":0,"total_score":17,"win":false,"opponent":"Corners"}
{"type":"game","round":8,"ticks":1710,"score":1,"total_score":18,"win":false,"opponent":"Corners"}
{"type":"game","round":9,"ticks":2180,"score":60,"total_score":78,"win":false,"opponent":"Corners"}
{"type":"game","round":10,"ticks":1364,"score":0,"total_score":78,"win":false,"opponent":"Corners"}
{"type":"game","round":1,"ticks":1426,"score":68,"total_score":68,"win":true,"opponent":"Crazy"}
{"type":"game","round":2,"ticks":1649,"score":70,"total_score":138,"win":true,"opponent":"Crazy"}
{"type":"game","round":3,"ticks":1456,"score":64,"total_score":202,"win":true,"opponent":"Crazy"}
{"type":"game","round":4,"ticks":1032,"score":61,"total_score":263,"win":true,"opponent":"Crazy"}
{"type":"game","round":5,"ticks":1710,"score":1,"total_score":264,"win":true,"opponent":"Crazy"}
{"type":"game","round":6,"ticks":941,"score":61,"total_score":325,"win":true,"opponent":"Crazy"}
{"type":"game","round":7,"ticks":1455,"score":61,"total_score":386,"win":true,"opponent":"Crazy"}
{"type":"game","round":8,"ticks":1323,"score":66,"total_score":452,"win":true,"opponent":"Crazy"}
{"type":"game","round":9,"ticks":1035,"score":5,"total_score":457,"win":true,"opponent":"Crazy"}
{"type":"game","round":10,"ticks":1457,"score":65,"total_score":522,"win":true,"opponent":"Crazy"}
{"type":"game","round":1,"ticks":1014,"score":14,"total_score":14,"win":false,"opponent":"Target"}
{"type":"game","round":2,"ticks":1074,"score":20,"total_score":34,"win":false,"opponent":"Target"}
{"type":"game","round":3,"ticks":996,"score":6,"total_score":40,"win":false,"opponent":"Target"}
{"type":"game","round":4,"ticks":1451,"score":0,"total_score":40,"win":false,"opponent":"Target"}
{"type":"game","round":5,"ticks":970,"score":25,"total_score":65,"win":false,"opponent":"Target"}
{"type":"game","round":6,"ticks":1451,"score":0,"total_score":65,"win":false,"opponent":"Target"}
{"type":"game","round":7,"ticks":1044,"score":1,"total_score":66,"win":false,"opponent":"Target"}
{"type":"game","round":8,"ticks":1119,"score":13,"total_score":79,"win":false,"opponent":"Target"}
{"type":"game","round":9,"ticks":1184,"score":15,"total_score":94,"win":false,"opponent":"Target"}
{"type":"game","round":10,"ticks":1028,"score":9,"total_score":103,"win":false,"opponent":"Target"}
SAC_LSTM_Bot_garage/make_twin.sh
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#!/usr/bin/env bash
# make_twin.sh — #54 mirror-twin sparring partner generator.
#
# Builds a self-contained `SacTwin` bot dir inside the sample-bots archive so
# RunTraining.java resolves it like any sample bot ($SAMPLE_BOTS_DIR/<name>).
# The twin is the SAME binary as SAC_LSTM_Bot but with:
# - distinct identity (SacTwin.json; SACLSTM_BOT_JSON overrides the baked-in
# src json — loadBotInfo gives json total precedence over env, #49 lesson)
# - its OWN weights dir, seeded from a FROZEN copy of weights/sac_best.zip
# (no checkpoint write races with the main bot)
# - its OWN round_counter.txt (main liveness guard untouched)
#
# Re-running resets the twin to the frozen baseline (reproducible opponent).
#
# Usage: ./make_twin.sh [target-archive-dir]
set -euo pipefail
SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
TARGET="${1:-${SAMPLE_BOTS_DIR:-/home/davide/Projects/tank-royale/sample-bots/java/build/archive}}/SacTwin"
BIN="$SCRIPT_DIR/SAC_LSTM_Bot" # nimble build -d:release output
SEED="$SCRIPT_DIR/weights/sac_best.zip" # frozen baseline
[ -x "$BIN" ] || { echo ">>> $BIN missing — run 'nimble build -d:release' first"; exit 1; }
[ -f "$SEED" ] || { echo ">>> $SEED missing — need at least one eval'd checkpoint"; exit 1; }
mkdir -p "$TARGET/weights"
cp "$BIN" "$TARGET/SAC_LSTM_Bot"
cat > "$TARGET/SacTwin.json" <<EOF
{
"name": "SacTwin",
"version": "0.1.0",
"authors": ["Davide Cappellini"],
"description": "Mirror-twin sparring partner of SAC_LSTM_Bot (#54), generated by make_twin.sh — own weights dir, frozen seed",
"homepage": "",
"countryCodes": ["IT"],
"gameTypes": ["classic", "melee", "1v1"],
"platform": "Nim",
"programmingLang": "Nim"
}
EOF
cat > "$TARGET/SacTwin.sh" <<'EOF'
#!/bin/sh
# Twin launcher (#54): identity + weights fully decoupled from the main bot.
DIR="$(cd "$(dirname "$0")" && pwd)"
export SACLSTM_BOT_JSON="$DIR/SacTwin.json"
export SACLSTM_WEIGHTS_PATH="$DIR/weights/sac_latest.zip"
# Freeze contract (#54): the twin is a FROZEN sparring partner, not a
# co-learner. Eval-mode gate (lever 4) suppresses all sendTrainingMsg traffic,
# so the twin never trains — not even in-RAM within a battle. Without this,
# any main-bot checkpoint-interval change would let twin drift accumulate.
export SACLSTM_EVAL_MODE=1
exec "$DIR/SAC_LSTM_Bot"
EOF
chmod +x "$TARGET/SacTwin.sh" "$TARGET/SAC_LSTM_Bot"
cp "$SEED" "$TARGET/weights/sac_latest.zip"
cp "$SEED" "$TARGET/weights/sac_best.zip"
echo 0 > "$TARGET/weights/round_counter.txt"
echo ">>> twin ready: $TARGET (seeded from $(stat -c %y "$SEED" | cut -d. -f1) snapshot of sac_best.zip)"
SAC_LSTM_Bot_garage/sac_train.sh
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#!/usr/bin/env bash
# sac_train.sh — #49 training orchestration for SAC_LSTM_Bot.
#
# Drives chunked self-play via tools/training_runner/RunTraining.java (which
# owns server lifecycle, opponent connection and dead-bot liveness detection
# through weights/round_counter.txt), samples opponents by weight per chunk,
# runs deterministic evaluation (SACLSTM_EVAL_MODE=1) every N chunks, and keeps
# the best checkpoint (weights/sac_best.zip) by a moving-average composite over
# the eval opponent set (campaign v2 lever 1, #59).
#
# Config (env vars):
# SAC_OPPONENTS "Name:weight,Name:weight,..." (default below)
# SAC_TOTAL_ROUNDS total training-round budget (default 100)
# SAC_CHUNK_SIZE rounds per RunTraining battle (default 10)
# SAC_EVAL_INTERVAL eval every N chunks (default 2)
# SAC_EVAL_ROUNDS rounds per evaluation battle (default 10)
# SAC_EVAL_OPPONENTS comma-separated eval set (default Corners,Crazy,Target)
# — each cycle evaluates EVERY one; results all land in
# eval_log.jsonl (lines carry "opponent":"Name")
# SAC_MAX_CRASHES consecutive crashes before abort (default 5)
# SAC_LOG_FILE / SAC_EVAL_LOG_FILE (JSON-lines logs)
# SACLSTM_* passed through to the bot (UTD_RATIO, BATCH_SIZE, ...)
set -uo pipefail
SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
REPO_ROOT="$(cd "$SCRIPT_DIR/.." && pwd)"
RUNNER_DIR="$REPO_ROOT/tools/training_runner"
JAR="${TANK_ROYALE_JAR:-/home/davide/Projects/tank-royale/runner/examples/lib/robocode-tankroyale-runner.jar}"
export PPO_BOT_DIR="$SCRIPT_DIR" # runner launches THIS bot dir
export BOT_NAME="${BOT_NAME:-SAC_LSTM_Bot}" # RunTraining result matching
export SAMPLE_BOTS_DIR="${SAMPLE_BOTS_DIR:-/home/davide/Projects/tank-royale/sample-bots/java/build/archive}"
# Liveness contract (#49): RunTraining.java watches $BOT_DIR/weights/round_counter.txt
# and integration.bumpRoundCounter() writes it next to the weights — so the bot's
# weights path is pinned here, NOT env-overridable.
export SACLSTM_WEIGHTS_PATH="$SCRIPT_DIR/weights/sac_latest.zip"
WEIGHTS_DIR="$(dirname "$SACLSTM_WEIGHTS_PATH")"
OPPONENTS="${SAC_OPPONENTS:-Corners:3,Crazy:2,RamFire:1,Target:1}"
TOTAL_ROUNDS="${SAC_TOTAL_ROUNDS:-100}"
CHUNK_SIZE="${SAC_CHUNK_SIZE:-10}"
EVAL_INTERVAL="${SAC_EVAL_INTERVAL:-2}"
EVAL_ROUNDS="${SAC_EVAL_ROUNDS:-10}"
EVAL_OPPONENTS="${SAC_EVAL_OPPONENTS:-Corners,Crazy,Target}"
MA_WINDOW=5 # lever 1 (#59): per-opponent moving average over last N evals
MAX_CRASHES="${SAC_MAX_CRASHES:-5}"
LOG_FILE="${SAC_LOG_FILE:-$SCRIPT_DIR/training_log.jsonl}"
EVAL_LOG_FILE="${SAC_EVAL_LOG_FILE:-$SCRIPT_DIR/eval_log.jsonl}"
CLASSES_DIR="/tmp/opencode/sac_train_classes"
echo "=== SAC_LSTM_Bot training harness ==="
echo "Opponents: $OPPONENTS | budget: $TOTAL_ROUNDS rounds in chunks of $CHUNK_SIZE"
echo "Eval: every $EVAL_INTERVAL chunks, $EVAL_ROUNDS rounds vs [$EVAL_OPPONENTS], MA-$MA_WINDOW composite best-gating"
echo "Weights: $SACLSTM_WEIGHTS_PATH"
# ── compile bot + java runner ─────────────────────────────────────────────────
(cd "$SCRIPT_DIR" && nimble build -d:release) || { echo ">>> bot build failed"; exit 1; }
mkdir -p "$WEIGHTS_DIR" "$CLASSES_DIR"
# Startup sweep: atomic saves leave sac_latest.zip.tmp.*.part corpses behind if
# a run is SIGKILLed (libzip modify-path, see notebook forensics) — clear them.
rm -f "$WEIGHTS_DIR"/sac_latest.zip.tmp.*.part "$WEIGHTS_DIR"/sac_latest.zip.tmp
javac -cp "$JAR" -d "$CLASSES_DIR" "$RUNNER_DIR/RunTraining.java" || { echo ">>> javac failed"; exit 1; }
# ── weighted opponent pick over "Name:w,Name:w" ───────────────────────────────
pick_opponent() {
local total=0 p name w r
local pairs
IFS=',' read -ra pairs <<< "$OPPONENTS"
for p in "${pairs[@]}"; do total=$(( total + ${p##*:} )); done
r=$(( RANDOM % total ))
for p in "${pairs[@]}"; do
name="${p%%:*}"; w="${p##*:}"
if (( r < w )); then echo "$name"; return; fi
r=$(( r - w ))
done
echo "${pairs[0]%%:*}"
}
run_battle() { # $1=opponent $2=rounds $3=log file
PPOB_LOG_FILE="$3" java -cp "$CLASSES_DIR:$JAR" RunTraining "$1" "$2"
}
# ── Lever 1 (#59): eval rotation + MA best-gating ─────────────────────────────
# best_score.txt FORMAT CHANGE: it used to store the single-opponent integer
# win rate (%); that semantics is retired. It now stores the COMPOSITE score —
# the mean over SAC_EVAL_OPPONENTS of each opponent's moving average (last
# MA_WINDOW eval win rates, %). sac_best.zip is rewritten only when the
# composite strictly improves.
ma_hist_file() { echo "$WEIGHTS_DIR/ma_history_$1.txt"; }
composite_of() { # reads one "w w w ..." history line per opponent on stdin
awk -v W="$MA_WINDOW" '
NF > 0 { n=NF; k=(n>W)?W:n; s=0; for(j=n-k+1;j<=n;j++) s+=$j; tot+=s/k; c++ }
END { if (c>0) printf "%.4f", tot/c; else print "-1" }'
}
eval_checkpoint() {
# ponytail: opponent names are split by whitespace — fine for Tank Royale bot
# names (no spaces); switch to a mapfile IFS=',\n' read if that ever changes.
local opps=(${EVAL_OPPONENTS//,/ })
local tmp="$EVAL_LOG_FILE.tmp" otmp opp wins rounds wr composite best
: > "$tmp"
for opp in "${opps[@]}"; do
otmp="$EVAL_LOG_FILE.$opp.tmp"
: > "$otmp"
echo ">>> [eval] $EVAL_ROUNDS deterministic rounds vs $opp"
if ! SACLSTM_EVAL_MODE=1 run_battle "$opp" "$EVAL_ROUNDS" "$otmp"; then
rm -f "$otmp" "$tmp"
echo ">>> [eval] crashed vs $opp — keeping previous best"
return 0
fi
wins=$(grep -c '"win":true' "$otmp" || true)
rounds=$(grep -c '"type":"game"' "$otmp" || true)
if (( rounds == 0 )); then
rm -f "$otmp" "$tmp"
echo ">>> [eval] no results vs $opp — keeping previous best"
return 0
fi
wr=$(( 100 * wins / rounds ))
echo ">>> [eval] win rate: $wins/$rounds ($wr%) vs $opp"
cat "$otmp" >> "$tmp"; rm -f "$otmp"
# Per-opponent history: append this cycle's win rate, keep last MA_WINDOW
# values on one line. Read FIRST, separately: `$(cat f)` inside a command
# redirected `> f` executes against the already-truncated file (bash sets
# up the redirect before running the substitution) — every cycle wiped the
# history back to a single leading-space value (#60). $hist is UNQUOTED on
# purpose: word-splitting turns the stored line into one value per line so
# tail keeps the last MA_WINDOW values.
local hist
hist="$(cat "$(ma_hist_file "$opp")" 2>/dev/null)"
printf '%s\n' $hist "$wr" \
| tail -n "$MA_WINDOW" | tr '\n' ' ' > "$(ma_hist_file "$opp")"
done
mv "$tmp" "$EVAL_LOG_FILE"
composite=$(for opp in "${opps[@]}"; do cat "$(ma_hist_file "$opp")"; echo; done | composite_of)
# ponytail: best-score state is a plain file next to the checkpoint; survives
# harness restarts, no lock needed (single harness instance assumed).
best=$(cat "$WEIGHTS_DIR/best_score.txt" 2>/dev/null)
[ -z "$best" ] && best=-1
if awk -v a="$composite" -v b="$best" 'BEGIN{exit !(a+0 > b+0)}' \
&& [ -f "$SACLSTM_WEIGHTS_PATH" ]; then
echo "$composite" > "$WEIGHTS_DIR/best_score.txt"
cp "$SACLSTM_WEIGHTS_PATH" "$WEIGHTS_DIR/sac_best.zip"
echo ">>> [eval] new best composite ($composite) -> sac_best.zip"
fi
}
NUM_CHUNKS=$(( (TOTAL_ROUNDS + CHUNK_SIZE - 1) / CHUNK_SIZE ))
fails=0
chunk=1
# while, not `for chunk in $(seq ...)`: a crash on the FINAL chunk must rerun
# it (#54 — seq list is exhausted by then, so ((chunk--));continue fell through
# and the harness exited 0 with the budget incomplete).
while (( chunk <= NUM_CHUNKS )); do
ROUNDS=$CHUNK_SIZE
(( TOTAL_ROUNDS - (chunk - 1) * CHUNK_SIZE < CHUNK_SIZE )) && \
ROUNDS=$(( TOTAL_ROUNDS - (chunk - 1) * CHUNK_SIZE ))
OPP=$(pick_opponent)
echo "=== Chunk $chunk/$NUM_CHUNKS: $ROUNDS rounds vs $OPP ==="
if ! run_battle "$OPP" "$ROUNDS" "$LOG_FILE"; then
fails=$(( fails + 1 ))
if (( fails >= MAX_CRASHES )); then
echo ">>> aborted: $fails consecutive crashes (bot process dying?)"
exit 1
fi
# Crash recovery: RunTraining's liveness detection exited; the bot reloads
# its latest checkpoint on restart, so just rerun this chunk.
echo ">>> crash #$fails — restarting chunk from latest checkpoint"
(( chunk-- )); continue
fi
fails=0
(( chunk % EVAL_INTERVAL == 0 )) && eval_checkpoint
((chunk += 1))
done
echo ">>> training complete: $NUM_CHUNKS chunks. Logs:"
echo " training: $LOG_FILE"
echo " eval: $EVAL_LOG_FILE"
[ -f "$WEIGHTS_DIR/sac_best.zip" ] && \
echo " best: $WEIGHTS_DIR/sac_best.zip (composite $(cat "$WEIGHTS_DIR/best_score.txt"))"
exit 0
SAC_LSTM_Bot_garage/src/SAC_LSTM_Bot.json
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{
"name": "SAC_LSTM_Bot",
"version": "0.1.0",
"authors": ["Davide Cappellini"],
"description": "SAC+LSTM Tank Royale bot — self-reported identity; MUST match the name in ../SAC_LSTM_Bot.json (booter identity) or the training runner never sees this bot join",
"homepage": "",
"countryCodes": ["IT"],
"gameTypes": ["classic", "melee", "1v1"],
"platform": "Nim",
"programmingLang": "Nim"
}
SAC_LSTM_Bot_garage/src/SAC_LSTM_Bot.nim
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## SAC_LSTM_Bot — Recurrent SAC-v2 bot (Gitea #48).
##
## Thread layout (decisions Q1–Q14, see integration.nim for plumbing):
## bot thread — this file's run(): inference only, <2ms/tick.
## training thread — permanent background SAC updates (integration.nim).
## I/O thread — atomic weight saves (integration.nim).
## No Arraymancer tensor ever crosses a thread boundary: the bot object and
## channels carry plain scalars / fixed arrays / plain seqs only.
import std/[os, math, random, algorithm]
import arraymancer except Linear
import tankroyale_botapi
import radar_lock
import SAC_LSTM_Bot/state
import SAC_LSTM_Bot/network
import SAC_LSTM_Bot/actions
import SAC_LSTM_Bot/rewards
import SAC_LSTM_Bot/integration
# Identity json: baked-in src json by default; SACLSTM_BOT_JSON lets a mirror
# twin (#54) boot the same binary under its own name (loadBotInfo gives the
# json total precedence over env, so the twin must point at its own file).
let botJsonPath = getEnv("SACLSTM_BOT_JSON",
currentSourcePath().parentDir / "SAC_LSTM_Bot.json")
# ── Colors (Recurrent Royalty palette) ───────────────────────────────────────
const
ColBody = fromHex("#7B2FBE")
ColTurret = fromHex("#FFD700")
ColGun = fromHex("#4A0E6B")
ColRadar = fromHex("#FFD700")
ColScan = fromHex("#FFB000")
ColBullet = fromHex("#FFC125")
ColTracks = fromHex("#2C2C34")
proc applyColors() =
setBodyColor(ColBody)
setTurretColor(ColTurret)
setGunColor(ColGun)
setRadarColor(ColRadar)
setScanColor(ColScan)
setBulletColor(ColBullet)
setTracksColor(ColTracks)
# ── Enemy bullet tracking ────────────────────────────────────────────────────
# PPO_Bot-proven pattern: dead-reckoned fixed buffer. getBulletStates() is NOT
# used from the bot thread — its seq refcount is shared with the main thread.
type InFlightBullet = object
x, y, vx, vy, power: float64
const MaxBotBullets = 4
const MaxHidden = 512 # ponytail: cap for the plain-array LSTM persistence; raise if SACLSTM_HIDDEN_SIZE > 512
# ── Bot type — PLAIN DATA ONLY on the shared object (no tensors/heap seqs:
# each round runs a fresh bot thread; heap blocks owned by the previous
# round's thread must not be freed from another thread) ─────────────────────
type SacBot = ref object of Bot
enemyBearing: float # last known absolute bearing to enemy
battleId: int # main thread bumps in onGameStarted; bot thread compares
seenBattle: int # bot-thread copy for battle-change detection
newBattleSent: bool # first scan of THIS battle emits NewBattle
hasContact: bool
enemy: EnemyData
ticksSinceScan: int
# per-step reward accumulators (consumed by the next tick's transition)
dmgDealt, dmgTaken, wastedPower: float64
wallHits, hits, ramTaken: int
# pending transition (episode spans the whole battle; round end is NOT a boundary)
hasLastTrans: bool
lastState: array[STATE_DIM, float32]
lastAction: array[ACTION_DIM, float32]
rn: RewardNormalizer # Welford running stats, persists across battles
bullets: array[MaxBotBullets, InFlightBullet]
bulletCount: int
hArr, cArr: array[MaxHidden, float32] # LSTM state across rounds; zeros at battle start
# ── Plain-array <-> tensor helpers (bot thread only) ──────────────────────────
proc stateToArr(t: Tensor[float32]): array[STATE_DIM, float32] =
for i in 0 ..< STATE_DIM: result[i] = t[i]
proc actionToArr(t: Tensor[float32]): array[ACTION_DIM, float32] =
for i in 0 ..< ACTION_DIM: result[i] = t[i]
proc hiddenToTensor(arr: array[MaxHidden, float32]; n: int): Tensor[float32] =
result = newTensor[float32](n)
for i in 0 ..< n: result[i] = arr[i]
proc tensorToHidden(t: Tensor[float32]; arr: var array[MaxHidden, float32]) =
for i in 0 ..< t.shape[0]: arr[i] = t[i]
# ── Reward ────────────────────────────────────────────────────────────────────
proc takeReward(bot: SacBot; win = false, loss = false): float32 =
## Consume accumulated step events -> Welford-normalized reward (#44).
## Lever 2 (#59): pass enemy distance (frac of arena diagonal) for the
## anti-charge term; sentinel 2.0 (> ChargeDistFrac) when no contact.
var distFrac = 2.0
if bot.hasContact:
let diag = hypot(getArenaWidth().float64, getArenaHeight().float64)
distFrac = hypot(bot.enemy.x - getX(), bot.enemy.y - getY()) / diag
let raw = computeReward(
damageInflicted = bot.dmgDealt,
damageReceived = bot.dmgTaken,
wallHitTicks = bot.wallHits,
wastedShotPower = bot.wastedPower,
hitCount = bot.hits,
ramTakenCount = bot.ramTaken,
enemyDistFrac = distFrac,
win = win, loss = loss)
# Lever-2 (#59) observability: env-gated one-liner for smoke/calibration
# greps — proves hit/ram/charge terms fire and shows raw magnitudes. File
# (not stderr): the battle runner swallows bot process streams.
# ponytail: grows unbounded if left on; keep off outside smokes.
if getEnv("SACLSTM_REWARD_DEBUG") == "1" and
(bot.hits > 0 or bot.ramTaken > 0 or (distFrac < ChargeDistFrac and bot.dmgDealt <= 0.0)):
try:
let f = open(getWeightsPath().parentDir.parentDir / "reward_debug.log", fmAppend)
f.writeLine("raw=" & $raw & " hits=" & $bot.hits & " ram=" & $bot.ramTaken &
" distFrac=" & $distFrac)
f.close()
except CatchableError:
discard
bot.dmgDealt = 0; bot.dmgTaken = 0; bot.wastedPower = 0; bot.wallHits = 0
bot.hits = 0; bot.ramTaken = 0
let norm = rewards.normalize(bot.rn, raw)
rewards.update(bot.rn, raw)
norm.float32
# ── Event handlers ────────────────────────────────────────────────────────────
# onGameStarted/onRoundStarted fire on the MAIN thread (bot thread not yet
# started or already joined) — plain-field writes only, no tensors here.
method onGameStarted*(bot: SacBot, e: GameStartedEventForBot) =
inc bot.battleId # bot thread zeroes LSTM + per-battle flags at next tick (Q4/Q12)
method onRoundStarted*(bot: SacBot, e: RoundStartedEvent) =
setAdjustRadarForBodyTurn(true)
setAdjustRadarForGunTurn(true)
radar_lock.init()
applyColors()
# Per-round reset ONLY. NOT the LSTM hidden state (persists across rounds, Q4);
# NOT hasLastTrans (the pending transition spans the round boundary — episode
# ends at battle end only).
bot.hasContact = false
bot.enemy = EnemyData()
bot.ticksSinceScan = 0
bot.bulletCount = 0
method onScannedBot*(bot: SacBot, e: ScannedBotEvent) =
bot.enemyBearing = directionTo(getX(), getY(), e.x, e.y)
# Same-tick radar lock: apply turn rate immediately so it takes effect this tick.
setRadarTurnRate(radar_lock.doRadar(getRadarDirection(), bot.enemyBearing))
# Fire detection: energy drop in [0.1, 3.0] between scans (PPO_Bot heuristic).
let prevE = if bot.hasContact: bot.enemy.energy else: e.energy
let drop = prevE - e.energy
bot.enemy.hasFired = bot.hasContact and drop >= 0.1 and drop <= 3.0
if bot.enemy.hasFired:
bot.enemy.lastFirePower = drop
# Keep previous-scan deltas before overwriting (state.nim derives accel/turn rate).
bot.enemy.prevSpeed = bot.enemy.speed
bot.enemy.prevDirection = bot.enemy.direction
bot.enemy.hasPrevScan = bot.hasContact
bot.enemy.x = e.x
bot.enemy.y = e.y
bot.enemy.direction = e.direction
bot.enemy.speed = e.speed
bot.enemy.energy = e.energy
bot.hasContact = true
bot.ticksSinceScan = 0
# Q12b/Q14+#49: one NewBattle per battle, numeric scannedBotId. Identity is
# keyed on getBotName(id) training-side (integration.opponentKey), numeric
# fallback in the pre-BotListUpdate window.
if not bot.newBattleSent:
bot.newBattleSent = true
discard sendTrainingMsg(TrainingMsg(kind: tmkNewBattle, enemyId: e.scannedBotId))
method onBulletHit*(bot: SacBot, e: BulletHitBotEvent) =
bot.dmgDealt += e.damage
inc bot.hits
method onHitByBullet*(bot: SacBot, e: HitByBulletEvent) =
bot.dmgTaken += e.damage
# Lever 2 (#59): anti-ram — every BotHitBotEvent receipt means a bot-bot
# collision happened and we ate RAM_DAMAGE (server deals 0.6 to both parties;
# only the hitter gets notified). Flat per-event penalty; being rammed without
# hitting back stays event-invisible.
# ponytail: enemy-initiated rams undetected — add energy-residual detection if
# v2 battle data shows ram-heavy losses.
method onHitBot*(bot: SacBot, e: BotHitBotEvent) =
inc bot.ramTaken
method onHitWall*(bot: SacBot, e: BotHitWallEvent) =
inc bot.wallHits
method onBulletHitWall*(bot: SacBot, e: BulletHitWallEvent) =
if e.bullet.ownerId == getMyId():
bot.wastedPower += e.bullet.power
method onGameAborted*(bot: SacBot) =
# Mid-round abort: drop the pending transition rather than leak it into the
# next battle's data.
bot.hasLastTrans = false
bot.dmgDealt = 0; bot.dmgTaken = 0; bot.wastedPower = 0; bot.wallHits = 0
bot.hits = 0; bot.ramTaken = 0
method onRoundEnded*(bot: SacBot, e: RoundEndedEventForBot) =
## Harness liveness signal (#49): RunTraining.java watches round_counter.txt
## and aborts the battle if it freezes (dead bot process). Main thread.
bumpRoundCounter()
method onGameEnded*(bot: SacBot, e: GameEndedEventForBot) =
## Battle end -> terminal transition with done=true. Main thread; the API has
## joined the bot thread before this fires, so these plain fields are quiescent.
if bot.hasLastTrans:
let r = bot.takeReward(win = e.results.rank == 1, loss = e.results.rank != 1)
discard sendTrainingMsg(TrainingMsg(kind: tmkTransition,
state: bot.lastState, action: bot.lastAction,
reward: r, nextState: bot.lastState, done: true))
bot.hasLastTrans = false
# ── Run loop (bot thread) ─────────────────────────────────────────────────────
method run(bot: SacBot) =
randomize()
# Per-thread locals: born and freed on THIS thread, every round. Nothing
# heap-owned survives the round boundary except the bot object's plain fields.
var actor: ActorNet
var actorReady = false
var myVersion = 0
var myHidden = 0
var flat: seq[float32]
while isRunning():
# Battle boundary (Q4/Q12): zero LSTM persistence + per-battle flags.
if bot.battleId != bot.seenBattle:
bot.seenBattle = bot.battleId
bot.newBattleSent = false
zeroMem(addr bot.hArr, sizeof(bot.hArr))
zeroMem(addr bot.cArr, sizeof(bot.cArr))
# Weight sync (Q7/Q11): always-latest; rebuild this thread's tensors on change.
if pullWeights(myVersion, myHidden, flat):
if myHidden > MaxHidden:
# hArr/cArr are fixed-capacity; a bigger SACLSTM_HIDDEN_SIZE would
# heap-overflow them in tensorToHidden. Loud misconfig beats corruption.
raise newException(ValueError, "SACLSTM_HIDDEN_SIZE=" & $myHidden &
" exceeds MaxHidden=" & $MaxHidden & " (bot-side LSTM persistence cap)")
var cur = 0
actor = actorFromFlat(flat, cur, myHidden)
actorReady = true
# Spawn an enemy bullet when a fresh scan shows they fired; then advance and
# prune the tracked bullets (positions feed state slots 22–33).
if bot.hasContact and bot.enemy.hasFired and bot.bulletCount < MaxBotBullets:
let p = bot.enemy.lastFirePower
let spd = 20.0 - 3.0 * p
let ang = arctan2(getY() - bot.enemy.y, getX() - bot.enemy.x)
bot.bullets[bot.bulletCount] = InFlightBullet(x: bot.enemy.x, y: bot.enemy.y,
vx: spd * cos(ang), vy: spd * sin(ang), power: p)
inc bot.bulletCount
let aW = float64(getArenaWidth())
let aH = float64(getArenaHeight())
var alive = 0
for i in 0 ..< bot.bulletCount:
let b = bot.bullets[i]
let nx = b.x + b.vx
let ny = b.y + b.vy
if nx >= 0.0 and nx <= aW and ny >= 0.0 and ny <= aH:
bot.bullets[alive] = InFlightBullet(x: nx, y: ny, vx: b.vx, vy: b.vy, power: b.power)
inc alive
bot.bulletCount = alive
# State build (35-dim, this thread's tensor).
inc bot.ticksSinceScan
var gs: GameState
gs.x = getX()
gs.y = getY()
gs.direction = getDirection()
gs.speed = getSpeed()
gs.energy = getEnergy()
gs.gunDirection = getGunDirection()
gs.gunHeat = getGunHeat()
gs.arenaWidth = aW
gs.arenaHeight = aH
gs.hasContact = bot.hasContact
gs.enemy = bot.enemy
gs.ticksSinceLastScan = bot.ticksSinceScan
var bd: array[MaxBotBullets, BulletData]
for i in 0 ..< bot.bulletCount:
bd[i] = BulletData(x: bot.bullets[i].x, y: bot.bullets[i].y, power: bot.bullets[i].power)
if bot.bulletCount > 1: # closest threats fill slots 0-2
bd.toOpenArray(0, bot.bulletCount - 1).sort(proc(a, b: BulletData): int =
cmp(hypot(a.x - gs.x, a.y - gs.y), hypot(b.x - gs.x, b.y - gs.y)))
gs.bulletCount = min(bot.bulletCount, 3)
for i in 0 ..< gs.bulletCount:
gs.bullets[i] = bd[i]
# Consume the fired pulse AFTER the state saw it (one shot -> one bullet).
bot.enemy.hasFired = false
let stateT = buildState(gs)
# Finalize the PREVIOUS transition: reward from events since the last tick,
# nextState is this tick's observation (PPO_Bot alignment).
if actorReady and bot.hasLastTrans:
discard sendTrainingMsg(TrainingMsg(kind: tmkTransition,
state: bot.lastState, action: bot.lastAction,
reward: bot.takeReward(), nextState: stateToArr(stateT), done: false))
bot.lastState = stateToArr(stateT)
if not actorReady:
setRadarTurnRate(45.0) # no weights yet (defensive; main pre-inits) — just sweep
go()
continue
# Inference: hidden state lives as plain arrays on the bot object (persists
# across rounds); tensors are rebuilt per tick on this thread.
let h = hiddenToTensor(bot.hArr, myHidden)
let c = hiddenToTensor(bot.cArr, myHidden)
let fwd = actor.actorForward(stateT, (h: h, c: c))
tensorToHidden(fwd.lstm.h, bot.hArr)
tensorToHidden(fwd.lstm.c, bot.cArr)
bot.lastAction = actionToArr(fwd.actions)
bot.hasLastTrans = true
# Actions -> intents (go() snapshots them at send time).
let mapped = mapActions(fwd.actions, getSpeed(), getGunHeat())
setTurnRate(mapped.turnRate)
setTargetSpeed(getSpeed() + mapped.acceleration) # actions.nim contract
setGunTurnRate(mapped.gunTurnRate)
if mapped.firePower > 0.0:
discard setFire(mapped.firePower)
if not bot.hasContact:
setRadarTurnRate(45.0) # sweep until first lock (onScannedBot overrides same-tick)
go()
# ── Entry point ───────────────────────────────────────────────────────────────
when isMainModule:
initIntegration() # spawn training + I/O threads, seed weight snapshot
var bot = SacBot()
start(bot, botJsonPath) # blocks until server disconnect
shutdownIntegration() # Shutdown msg -> final save -> joins
SAC_LSTM_Bot_garage/src/SAC_LSTM_Bot/.gitkeep
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## actions.nim — map raw network output (4 tanh values) to bot intent fields.
##
## Note on acceleration vs targetSpeed:
## TankRoyale uses setTargetSpeed(), not setAcceleration().
## The mapped `acceleration` field is a delta; callers must compute:
## newTargetSpeed = clamp(currentSpeed + acceleration, -8.0, 8.0)
## and call setTargetSpeed(newTargetSpeed).
import arraymancer
const ACTION_DIM* = 4
type
MappedActions* = object
turnRate*: float ## degrees/tick, speed-aware; [-10, 10] at speed 0
acceleration*: float ## delta speed in [-2, +1]; caller adds to currentSpeed
gunTurnRate*: float ## degrees/tick in [-20, 20]
firePower*: float ## 0 = don't fire; (0.1, 3.0] = fire with this power
proc mapActions*(networkOutput: Tensor[float32],
currentSpeed: float,
gunHeat: float): MappedActions =
## networkOutput: [4] tensor of tanh values in [-1, 1].
let a0 = networkOutput[0].float
let a1 = networkOutput[1].float
let a2 = networkOutput[2].float
let a3 = networkOutput[3].float
result.turnRate = a0 * (10.0 - 0.75 * abs(currentSpeed))
# asymmetric accel: [-1,1] -> [-2, +1] via (value * 1.5 - 0.5)
result.acceleration = a1 * 1.5 - 0.5
result.gunTurnRate = a2 * 20.0
if a3 > 0.0 and gunHeat <= 0.0:
result.firePower = a3 * 2.9 + 0.1
else:
result.firePower = 0.0
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## integration.nim — #48 thread plumbing for SAC_LSTM_Bot.
##
## Threads added by the bot (on top of the bot API's main/bot/sender):
## training thread — permanent, drain-then-train loop, owns SACTrainer +
## ReplayBuffer. Q1/Q2/Q10/Q12.
## I/O thread — cap-1 channel of weight snapshots, atomic zip saves. Q5.
##
## Cross-thread payloads are plain arrays/seqs ONLY. No Arraymancer tensor ever
## crosses a thread boundary: Tensor is a ref type and ORC refcounts are
## non-atomic — sharing them across threads is SIGSEGV territory (PPO_Bot,
## empirically confirmed). Each thread builds its own tensors from plain data.
## Decisions Q1–Q14: Gitea #48.
import arraymancer except Linear
import std/[locks, os, math, random, strutils, times]
import tankroyale_botapi # getBotName (#49 name-based opponent identity)
import SAC_LSTM_Bot/network
import SAC_LSTM_Bot/state # STATE_DIM
import SAC_LSTM_Bot/actions # ACTION_DIM
import SAC_LSTM_Bot/replay_buffer
import SAC_LSTM_Bot/training
import SAC_LSTM_Bot/weights
# ── Config ────────────────────────────────────────────────────────────────────
proc getUtdRatio*(): int =
## Gradient steps per drained transition (Q2). 200 ticks -> 200 steps at 1.
parseInt(getEnv("SACLSTM_UTD_RATIO", "1"))
proc getBatchSize*(): int =
# ponytail: 16 is an unprofiled guess sized so UTD=1 keeps up with 30tps;
# env knob is the calibration point if steps/sec falls short.
parseInt(getEnv("SACLSTM_BATCH_SIZE", "16"))
proc getSaveInterval*(): int =
parseInt(getEnv("SACLSTM_SAVE_INTERVAL", "500"))
proc getWeightsPath*(): string =
getEnv("SACLSTM_WEIGHTS_PATH",
currentSourcePath().parentDir / "weights" / "sac_latest.zip")
proc opponentKey*(enemyId: int): string =
## Q14 follow-up (#49): name-based opponent identity from the v1.0.1
## BotListUpdate table; numeric-id fallback for the window before the first
## update arrives (getBotName still returns "" then). Called on the training
## thread — the lookup is lock-guarded in the API, no cross-thread refs.
let name = getBotName(enemyId)
if name.len > 0: name else: $enemyId
proc bumpRoundCounter*() =
## Liveness signal for tools/training_runner/RunTraining.java (#49): one
## increment per round end; the runner aborts when it freezes (dead bot).
# ponytail: non-atomic read-modify-write; single writer (main thread) and the
# runner re-polls every 500ms with multi-round tolerance, torn reads self-heal.
let p = getWeightsPath().parentDir / "round_counter.txt"
var n = 0
try:
n = parseInt(readFile(p).strip())
except CatchableError:
discard # absent/garbage -> start at 1
try:
createDir(p.parentDir)
writeFile(p, $(n + 1))
except CatchableError:
discard # counter is best-effort liveness; never kill an event handler
# ── Channel message (plain data only) ────────────────────────────────────────
type
TrainingMsgKind* = enum tmkTransition, tmkNewBattle, tmkShutdown
TrainingMsg* = object
case kind*: TrainingMsgKind
of tmkTransition:
state*: array[STATE_DIM, float32] # Q13 plain arrays, no tensors
action*: array[ACTION_DIM, float32]
reward*: float32
nextState*: array[STATE_DIM, float32]
done*: bool # true only at battle end
of tmkNewBattle:
enemyId*: int # Q14 numeric scannedBotId
of tmkShutdown:
discard
proc arrToTensor*[N: static int](arr: array[N, float32]): Tensor[float32] =
result = newTensor[float32](N)
for i in 0 ..< N: result[i] = arr[i]
# ── Flat weight snapshots ─────────────────────────────────────────────────────
# Layout mirrors network.nim's fixed architecture (fc1 -> hiddenDim-wide LSTM
# with [4h, 2h] combined weights, 128-wide fc2, 4-out heads). The asserts catch
# layout drift if network.nim shapes ever change.
proc actorSize*(h: int): int = 8*h*h + 168*h + 1160
proc criticSize*(h: int): int = 8*h*h + 172*h + 257
proc putT(t: Tensor[float32]; dst: var seq[float32]; c: var int) =
for v in t:
dst[c] = v
inc c
proc takeT(src: seq[float32]; c: var int; rows, cols: int): Tensor[float32] =
# seq slice copies, then toTensor copies again: result owns its memory —
# never a view into src (src may be a cross-thread buffer).
let n = rows * cols
result = src[c ..< c + n].toTensor().reshape(rows, cols)
c += n
proc takeV(src: seq[float32]; c: var int; n: int): Tensor[float32] =
## Rank-1 vector (biases) — reshape(n) keeps rank 1.
result = src[c ..< c + n].toTensor().reshape(n)
c += n
proc packActor*(a: ActorNet; dst: var seq[float32]; c: var int) =
putT(a.fc1.w, dst, c); putT(a.fc1.b, dst, c)
putT(a.lstm.wCombined, dst, c); putT(a.lstm.bCombined, dst, c)
putT(a.fc2.w, dst, c); putT(a.fc2.b, dst, c)
putT(a.muHead.w, dst, c); putT(a.muHead.b, dst, c)
putT(a.logStdHead.w, dst, c); putT(a.logStdHead.b, dst, c)
proc packCritic*(net: CriticNet; dst: var seq[float32]; c: var int) =
putT(net.fc1.w, dst, c); putT(net.fc1.b, dst, c)
putT(net.lstm.wCombined, dst, c); putT(net.lstm.bCombined, dst, c)
putT(net.fc2.w, dst, c); putT(net.fc2.b, dst, c)
putT(net.fc3.w, dst, c); putT(net.fc3.b, dst, c)
proc actorFromFlat*(src: seq[float32]; c: var int; h: int): ActorNet =
result.fc1.w = takeT(src, c, h, 35)
result.fc1.b = takeV(src, c, h)
result.lstm.wCombined = takeT(src, c, 4*h, 2*h)
result.lstm.bCombined = takeV(src, c, 4*h)
result.fc2.w = takeT(src, c, 128, h)
result.fc2.b = takeV(src, c, 128)
result.muHead.w = takeT(src, c, 4, 128)
result.muHead.b = takeV(src, c, 4)
result.logStdHead.w = takeT(src, c, 4, 128)
result.logStdHead.b = takeV(src, c, 4)
result.lstm.hiddenDim = result.lstm.bCombined.size div 4 # same as weights.nim loadLSTMCell
result.hiddenDim = h
assert c == actorSize(h), "actor flat layout drift"
proc criticFromFlat*(src: seq[float32]; c: var int; h: int): CriticNet =
result.fc1.w = takeT(src, c, h, 39)
result.fc1.b = takeV(src, c, h)
result.lstm.wCombined = takeT(src, c, 4*h, 2*h)
result.lstm.bCombined = takeV(src, c, 4*h)
result.fc2.w = takeT(src, c, 128, h)
result.fc2.b = takeV(src, c, 128)
result.fc3.w = takeT(src, c, 1, 128)
result.fc3.b = takeV(src, c, 1)
result.lstm.hiddenDim = result.lstm.bCombined.size div 4
result.hiddenDim = h
type
FullSnap* = object
hiddenDim*: int
data*: seq[float32] # actor | critic1 | critic2 | targetCritic1 | targetCritic2 | alpha
proc packFull*(t: SACTrainer): FullSnap =
let h = t.actor.hiddenDim
result.hiddenDim = h
result.data = newSeq[float32](actorSize(h) + 4 * criticSize(h) + 1)
var c = 0
packActor(t.actor, result.data, c)
packCritic(t.critic1, result.data, c)
packCritic(t.critic2, result.data, c)
packCritic(t.targetCritic1, result.data, c)
packCritic(t.targetCritic2, result.data, c)
assert abs(t.alpha().float64 - exp(t.logAlpha.float64)) < 1e-6
result.data[c] = t.alpha()
inc c
assert c == result.data.len, "full snapshot layout drift"
proc unpackFull*(fs: FullSnap):
tuple[a: ActorNet, c1, c2, t1, t2: CriticNet, alpha: float32] =
var c = 0
result.a = actorFromFlat(fs.data, c, fs.hiddenDim)
result.c1 = criticFromFlat(fs.data, c, fs.hiddenDim)
result.c2 = criticFromFlat(fs.data, c, fs.hiddenDim)
result.t1 = criticFromFlat(fs.data, c, fs.hiddenDim)
result.t2 = criticFromFlat(fs.data, c, fs.hiddenDim)
result.alpha = fs.data[c]
# ── Shared weight snapshot (training thread writes, bot thread copies out) ────
# Q7/Q11: Lock + always-latest semantics. `data` is allocated ONCE and written
# IN PLACE under gWeightLock — it is never reassigned, so the shared heap block
# never sees cross-thread refcount traffic. Readers copy element-wise out.
type
WeightSnapshot* = object
hiddenDim*: int
version*: int
data*: seq[float32]
var gWeightLock: Lock
var gSharedSnap: WeightSnapshot
var gTrainChan: Channel[TrainingMsg]
var gSaveChan: Channel[FullSnap]
var gTrainingThread: Thread[void]
var gIoThread: Thread[void]
var gInitialFull: FullSnap # built on main before spawn; read-once after (happens-before)
var gWeightsPath: string
proc pullWeights*(myVersion: var int; hidden: var int;
flat: var seq[float32]): bool =
## Copy the latest actor snapshot out under the lock. Returns true when a new
## version arrived (caller rebuilds its tensors on ITS OWN thread).
withLock(gWeightLock):
if gSharedSnap.version == myVersion:
return false
if flat.len != gSharedSnap.data.len:
flat = newSeq[float32](gSharedSnap.data.len) # caller-thread-owned buffer
for i in 0 ..< flat.len:
flat[i] = gSharedSnap.data[i]
myVersion = gSharedSnap.version
hidden = gSharedSnap.hiddenDim
true
proc evalModeActive*(): bool {.inline.} =
## Lever 4 (#59): the harness's deterministic eval battles already run the bot
## with SACLSTM_EVAL_MODE=1 (sac_train.sh eval_checkpoint, mechanism from #49).
## While set, eval ticks must NOT feed the trainer — transitions would pollute
## the replay buffer with eval-only data and trigger gradient updates.
getEnv("SACLSTM_EVAL_MODE") == "1"
proc sendTrainingMsg*(msg: TrainingMsg): bool {.inline.} =
## Bot-side enqueue (cap-256, drops on overflow per Q10). Thread-safe.
## Lever 4 (#59): fully suppressed in eval mode — NewBattle drops too, so an
## eval battle can neither add transitions nor clear/retarget the buffer.
if evalModeActive(): return false
gTrainChan.trySend(msg)
# ── Training state (testable without threads) ─────────────────────────────────
type
TrainState* = object
trainer*: SACTrainer
buf*: ReplayBuffer
lastEnemyKey*: string # opponent identity key (#49 name-based, Q14)
stepCount*: int
nextSave*: int
proc trainerFromFull*(initial: FullSnap): SACTrainer =
let (a, c1, c2, t1, t2, alpha) = unpackFull(initial)
result.actor = a
result.critic1 = c1
result.critic2 = c2
result.targetCritic1 = t1
result.targetCritic2 = t2
assert alpha > 0.0'f32, "checkpoint alpha must be positive"
result.logAlpha = ln(alpha)
result.targetEntropy = getTargetEntropy()
result.tau = getTau()
result.lrActor = getLrActor()
result.lrCritic = getLrCritic()
result.lrAlpha = getLrAlpha()
result.gamma = getGamma()
result.adam = initSACAdamStates(result.actor, result.critic1, result.critic2)
# ponytail: adam momentum not carried in the flat snapshot — optimizer restarts
# fresh each process; switch to saveCheckpoint/loadCheckpoint end-to-end when
# resume quality matters (#49 harness owns checkpoint management).
proc initTrainState*(initial: FullSnap): TrainState =
result.trainer = trainerFromFull(initial)
result.buf = newReplayBuffer(getBufferCapacity(), STATE_DIM, ACTION_DIM)
result.lastEnemyKey = ""
result.nextSave = getSaveInterval()
# ── Training-loss metrics (campaign v2 lever 3, #59) ──────────────────────────
proc metricsFilePath*(): string =
## Sits next to the weights dir's parent: SAC_LSTM_Bot/training_metrics.jsonl
## under the #49 harness (weights live in SAC_LSTM_Bot/weights/).
getWeightsPath().parentDir.parentDir / "training_metrics.jsonl"
proc metricsLine*(epoch: float64; stepCount, bufferLen, drained, gradSteps: int;
m: SACMetrics): string =
## One JSONL line with exactly the scalars SACTrainer.sacUpdate exposes
## (#59 lever 3 — SACMetrics was already returned, no trainer change needed):
## losses/alpha averaged over this pass's gradient steps, buffer size from
## replay_buffer.len, cumulative step count and drained transition count.
"{\"epoch\":" & $epoch &
",\"steps\":" & $stepCount &
",\"buffer_size\":" & $bufferLen &
",\"drained\":" & $drained &
",\"grad_steps\":" & $gradSteps &
",\"critic_loss\":" & $m.criticLoss &
",\"actor_loss\":" & $m.actorLoss &
",\"alpha_loss\":" & $m.alphaLoss &
",\"alpha\":" & $m.alpha & "}"
proc appendMetricsLine(st: TrainState; drained, gradSteps: int; m: SACMetrics) =
## Lever 3 (#59): one append per trainPass (never per gradient step). Open,
## write, close — cheap and crash-tolerant; a metrics failure never kills
## training.
try:
let f = open(metricsFilePath(), fmAppend)
f.writeLine(metricsLine(epochTime(), st.stepCount, st.buf.len,
drained, gradSteps, m))
f.close()
except CatchableError:
discard
proc handleTrainingMsg*(st: var TrainState; msg: TrainingMsg): bool =
## Process one message. Returns false for Shutdown (caller stops).
## Tensors are born HERE from the message's plain arrays — training thread only.
case msg.kind
of tmkTransition:
st.buf.add(Transition(
state: msg.state.arrToTensor,
action: msg.action.arrToTensor,
reward: msg.reward,
nextState: msg.nextState.arrToTensor,
done: msg.done))
of tmkNewBattle:
# Q12a: keep the buffer if the opponent is unchanged, clear otherwise.
# Identity keyed on NAME (#49); numeric-id fallback pre-BotListUpdate.
let key = opponentKey(msg.enemyId)
if key != st.lastEnemyKey:
st.buf.clear()
st.lastEnemyKey = key
of tmkShutdown:
return false
true
proc trainPass*(st: var TrainState; drained: int) =
## UTD gradient steps for the transitions drained this pass, then publish the
## latest actor to the shared snapshot and request periodic disk saves.
if drained <= 0 or not st.buf.canSample:
return
let steps = drained * getUtdRatio() # Q2
var gradSteps = 0
var sumCritic, sumActor, sumAlphaLoss, sumAlpha = 0.0'f32
for i in 1 .. steps:
let seqs = st.buf.sampleSequences(getBatchSize())
if seqs.len == 0:
break
let m = sacUpdate(st.trainer, seqs)
sumCritic += m.criticLoss; sumActor += m.actorLoss
sumAlphaLoss += m.alphaLoss; sumAlpha += m.alpha
inc gradSteps
inc st.stepCount
# Save check INSIDE the step loop (#56 launch finding): at production sizes
# (hidden 256 ⇒ ~1 s/step) a drain burst queues minutes of steps; checking
# only between passes meant the process died mid-loop before stepCount ever
# reached nextSave — zero checkpoints persisted for the whole campaign.
# Mid-loop checks + SAVE_INTERVAL≤20 (#54) keep saves ~20 s apart.
if st.stepCount >= st.nextSave:
st.nextSave += getSaveInterval()
var full = packFull(st.trainer)
discard gSaveChan.trySend(move(full)) # cap-1: drop if I/O thread is busy (Q5)
if gradSteps > 0:
# Lever 3 (#59): one metrics line per pass, losses averaged over its steps.
appendMetricsLine(st, drained, gradSteps, SACMetrics(
criticLoss: sumCritic / gradSteps.float32,
actorLoss: sumActor / gradSteps.float32,
alphaLoss: sumAlphaLoss / gradSteps.float32,
alpha: sumAlpha / gradSteps.float32))
# Publish latest actor (Q7): in-place write under the lock, bump version.
withLock(gWeightLock):
assert gSharedSnap.hiddenDim == st.trainer.actor.hiddenDim,
"snapshot/trainer hidden size mismatch"
var c = 0
packActor(st.trainer.actor, gSharedSnap.data, c)
inc gSharedSnap.version
# ── Threads ───────────────────────────────────────────────────────────────────
proc trainingThreadEntry() {.thread.} =
{.cast(gcsafe).}:
randomize()
var st = initTrainState(gInitialFull)
var running = true
while running:
let first = gTrainChan.recv() # block until traffic (no busy spin)
var drained = 0
var msg = first
while running:
if msg.kind == tmkTransition:
inc drained
if not handleTrainingMsg(st, msg):
running = false # Shutdown
break
let (more, nxt) = gTrainChan.tryRecv()
if not more:
break # drained — now train (Q10)
msg = nxt
if not running:
var full = packFull(st.trainer) # final save request, then exit
discard gSaveChan.trySend(move(full))
break
trainPass(st, drained)
proc ioThreadEntry() {.thread.} =
{.cast(gcsafe).}:
while true:
let fs = gSaveChan.recv() # blocks; exits via empty-data sentinel
if fs.data.len == 0:
break
let (a, c1, c2, t1, t2, alpha) = unpackFull(fs)
saveWeights(gWeightsPath, a, c1, c2, t1, t2, alpha) # atomic zip (weights.nim)
# ── Lifecycle ─────────────────────────────────────────────────────────────────
proc randomFull(): FullSnap =
let t = initSACTrainer(STATE_DIM, ACTION_DIM) # random nets, born + freed here
packFull(t)
proc loadOrInitFull(): FullSnap =
let path = getWeightsPath()
if fileExists(path):
try:
let cp = loadCheckpoint(path)
var t = initSACTrainer(STATE_DIM, ACTION_DIM) # env hyperparams
t.actor = cp.actor
t.critic1 = cp.critic1
t.critic2 = cp.critic2
t.targetCritic1 = cp.targetCritic1
t.targetCritic2 = cp.targetCritic2
assert cp.alpha > 0.0'f32, "checkpoint alpha must be positive"
t.logAlpha = ln(cp.alpha)
result = packFull(t)
except Exception as e:
stderr.writeLine "[sac] checkpoint load failed (" & e.msg & ") — random init"
result = randomFull()
else:
result = randomFull()
proc initIntegration*() =
## Open channels, build the initial weight snapshot, spawn both threads.
## Call once from the main module before start().
gWeightsPath = getWeightsPath()
gInitialFull = loadOrInitFull()
gSharedSnap.hiddenDim = gInitialFull.hiddenDim
gSharedSnap.data = newSeq[float32](actorSize(gInitialFull.hiddenDim))
for i in 0 ..< gSharedSnap.data.len: # element-wise: no refcount traffic
gSharedSnap.data[i] = gInitialFull.data[i]
gSharedSnap.version = 1
initLock(gWeightLock)
gTrainChan.open(256) # Q10 cap-256
gSaveChan.open(1) # Q5 cap-1
# Lever 4 (#59): one-time visibility for the suppression gate (see
# sendTrainingMsg) — the eval bot trains nothing by design.
if evalModeActive():
stderr.writeLine "[sac] SACLSTM_EVAL_MODE=1 — training input suppressed (lever 4, #59)"
createThread(gTrainingThread, trainingThreadEntry)
createThread(gIoThread, ioThreadEntry)
proc shutdownIntegration*() =
## Stop both threads cleanly. Called after the bot disconnects (start returned).
while gTrainChan.tryRecv().dataAvailable:
discard # drop pending transitions — process exiting
discard gTrainChan.trySend(TrainingMsg(kind: tmkShutdown))
joinThread(gTrainingThread) # training requests one final save
# Nim's recv() blocks even on closed channels, so the I/O thread exits via an
# empty-data sentinel. Retry while it is still busy saving: every failed
# trySend means a save is in flight and will be consumed, so this terminates.
var stop = FullSnap(hiddenDim: -1)
while not gSaveChan.trySend(move(stop)):
sleep(50)
stop = FullSnap(hiddenDim: -1)
joinThread(gIoThread)
gTrainChan.close()
SAC_LSTM_Bot_garage/src/SAC_LSTM_Bot/network
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## network.nim — LSTM-based Actor and dual Critic for SAC-v2.
## No autograd; inference only. Manual LSTM cell from scratch.
import arraymancer
import std/[math, random, os, strutils]
# ── Configuration ─────────────────────────────────────────────────────────────
proc getHiddenSize*(): int =
let s = getEnv("SACLSTM_HIDDEN_SIZE", "256")
result = parseInt(s)
proc isEvalMode*(): bool =
getEnv("SACLSTM_EVAL_MODE", "0") == "1"
# ── Types ─────────────────────────────────────────────────────────────────────
type
Linear* = object
w*, b*: Tensor[float32] # w: [out, in], b: [out]
LSTMCell* = object
## Combined weight matrix Wi|Wf|Wg|Wo stacked: [4*hidden, input+hidden]
## Combined bias stacked: [4*hidden]
wCombined*: Tensor[float32]
bCombined*: Tensor[float32]
hiddenDim*: int
ActorNet* = object
fc1*: Linear
lstm*: LSTMCell
fc2*: Linear
muHead*: Linear
logStdHead*: Linear
hiddenDim*: int
CriticNet* = object
fc1*: Linear
lstm*: LSTMCell
fc2*: Linear
fc3*: Linear
hiddenDim*: int
LSTMState* = tuple[h, c: Tensor[float32]] # each [hiddenDim]
# ── Init helpers ──────────────────────────────────────────────────────────────
proc initLinear*(inDim, outDim: int; scale: float32): Linear =
result.w = randomNormalTensor[float32]([outDim, inDim]) *. scale
result.b = zeros[float32](outDim)
proc initLinearHe*(inDim, outDim: int): Linear =
initLinear(inDim, outDim, sqrt(2.0'f32 / inDim.float32))
proc initLinearOut*(inDim, outDim: int): Linear =
initLinear(inDim, outDim, sqrt(1.0'f32 / inDim.float32))
proc initLSTMCell*(inputDim, hiddenDim: int): LSTMCell =
result.hiddenDim = hiddenDim
let fanIn = (inputDim + hiddenDim).float32
let scale = sqrt(1.0'f32 / fanIn)
result.wCombined = randomNormalTensor[float32]([4 * hiddenDim, inputDim + hiddenDim]) *. scale
result.bCombined = zeros[float32](4 * hiddenDim)
proc zeroState*(hiddenDim: int): LSTMState =
result = (h: zeros[float32](hiddenDim), c: zeros[float32](hiddenDim))
proc initActorNet*(stateDim: int): ActorNet =
let h = getHiddenSize()
result.hiddenDim = h
result.fc1 = initLinearHe(stateDim, h)
result.lstm = initLSTMCell(h, h)
result.fc2 = initLinearHe(h, 128)
result.muHead = initLinearOut(128, 4)
result.logStdHead = initLinearOut(128, 4)
proc initCriticNet*(stateDim, actionDim: int): CriticNet =
let h = getHiddenSize()
result.hiddenDim = h
result.fc1 = initLinearHe(stateDim + actionDim, h)
result.lstm = initLSTMCell(h, h)
result.fc2 = initLinearHe(h, 128)
result.fc3 = initLinearOut(128, 1)
# ── Forward helpers ───────────────────────────────────────────────────────────
proc linear*(l: Linear; x: Tensor[float32]): Tensor[float32] =
l.w * x + l.b
proc relu*(x: Tensor[float32]): Tensor[float32] =
x.map(proc(v: float32): float32 = max(0.0'f32, v))
proc sigmoid*(x: Tensor[float32]): Tensor[float32] =
x.map(proc(v: float32): float32 = 1.0'f32 / (1.0'f32 + exp(-v)))
proc tanhT*(x: Tensor[float32]): Tensor[float32] =
x.map(proc(v: float32): float32 = tanh(v))
proc lstmStep*(cell: LSTMCell; x, h, c: Tensor[float32]): LSTMState =
## x: [inputDim], h/c: [hiddenDim] → h', c': [hiddenDim]
let xh = concat(x, h, axis = 0) # [inputDim + hiddenDim]
let gates = cell.wCombined * xh + cell.bCombined # [4*hidden]
let hd = cell.hiddenDim
let iGate = sigmoid(gates[0 ..< hd])
let fGate = sigmoid(gates[hd ..< 2*hd])
let gGate = tanhT(gates[2*hd ..< 3*hd])
let oGate = sigmoid(gates[3*hd ..< 4*hd])
let cPrime = fGate *. c + iGate *. gGate
let hPrime = oGate *. tanhT(cPrime)
result = (h: hPrime, c: cPrime)
# ── Actor forward ─────────────────────────────────────────────────────────────
const
LOG_STD_MIN = -5.0'f32
LOG_STD_MAX = 2.0'f32
LOG_PROB_EPS = 1e-6'f32
proc actorForward*(net: ActorNet; state: Tensor[float32]; lstm: LSTMState;
deterministic = false):
tuple[actions: Tensor[float32]; logProb: float32; lstm: LSTMState] =
## state: [stateDim], lstm: (h,c) each [hiddenDim]
## Returns actions [4], scalar logProb, updated (h',c').
let h1 = relu(net.fc1.linear(state))
let lstmOut = lstmStep(net.lstm, h1, lstm.h, lstm.c)
let h2 = relu(net.fc2.linear(lstmOut.h))
let mu = net.muHead.linear(h2)
let logStdRaw = net.logStdHead.linear(h2)
let logStd = logStdRaw.map(proc(v: float32): float32 = clamp(v, LOG_STD_MIN, LOG_STD_MAX))
if deterministic or isEvalMode():
let actions = tanhT(mu)
return (actions: actions, logProb: 0.0'f32, lstm: lstmOut)
# Reparameterization: z = mu + std * eps, action = tanh(z)
let std = logStd.map(proc(v: float32): float32 = exp(v))
var actions = newTensor[float32](4)
var logProb = 0.0'f32
let twoPiLog = 0.5'f32 * ln(2.0'f32 * PI.float32)
for i in 0 ..< 4:
let eps = gauss(0.0'f64, 1.0'f64).float32
let z = mu[i] + std[i] * eps
actions[i] = tanh(z)
# log N(z | mu, std) - log(1 - tanh²(z) + eps)
let diff = (z - mu[i]) / std[i]
let logNorm = -0.5'f32 * diff * diff - ln(std[i]) - twoPiLog
let tanhCorr = ln(1.0'f32 - actions[i] * actions[i] + LOG_PROB_EPS)
logProb += logNorm - tanhCorr
result = (actions: actions, logProb: logProb, lstm: lstmOut)
# ── Critic forward ────────────────────────────────────────────────────────────
proc criticForward*(net: CriticNet; stateAction: Tensor[float32]; lstm: LSTMState):
tuple[q: float32; lstm: LSTMState] =
## stateAction: [stateDim + actionDim], lstm: (h,c) each [hiddenDim]
let h1 = relu(net.fc1.linear(stateAction))
let lstmOut = lstmStep(net.lstm, h1, lstm.h, lstm.c)
let h2 = relu(net.fc2.linear(lstmOut.h))
let q = net.fc3.linear(h2)
result = (q: q[0], lstm: lstmOut)
SAC_LSTM_Bot_garage/src/SAC_LSTM_Bot/replay_buffer.nim
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## replay_buffer.nim — sequential ring buffer for off-policy SAC+LSTM training.
##
## Stores transitions and samples contiguous sequences for recurrent training.
## Sequences NEVER cross battle boundaries (done=true).
##
## Config env vars:
## SACLSTM_BUFFER_CAPACITY (default: 500_000)
## SACLSTM_BURN_IN (default: 8)
## SACLSTM_TRAIN_WINDOW (default: 16)
import arraymancer
import std/[os, strutils, random]
# ── Config ────────────────────────────────────────────────────────────────────
proc getBufferCapacity*(): int =
parseInt(getEnv("SACLSTM_BUFFER_CAPACITY", "500000"))
proc getBurnIn*(): int =
parseInt(getEnv("SACLSTM_BURN_IN", "8"))
proc getTrainWindow*(): int =
parseInt(getEnv("SACLSTM_TRAIN_WINDOW", "16"))
# ── Types ─────────────────────────────────────────────────────────────────────
type
Transition* = object
state*: Tensor[float32] # [stateDim]
action*: Tensor[float32] # [actionDim]
reward*: float32
nextState*: Tensor[float32] # [stateDim]
done*: bool # true = battle end
Sequence* = object
burnIn*: seq[Transition] # first burnIn steps (for LSTM warm-up)
train*: seq[Transition] # next trainWindow steps (for gradient computation)
ReplayBuffer* = object
## Ring buffer. `head` is the next write position. `count` tracks fill level.
transitions: seq[Transition]
capacity: int
stateDim: int
actionDim: int
head: int # next write index
count: int # number of valid transitions stored
burnIn: int
trainWindow: int
# ── Construction ──────────────────────────────────────────────────────────────
proc newReplayBuffer*(capacity, stateDim, actionDim: int;
burnIn = getBurnIn();
trainWindow = getTrainWindow()): ReplayBuffer =
result.capacity = capacity
result.stateDim = stateDim
result.actionDim = actionDim
result.burnIn = burnIn
result.trainWindow = trainWindow
result.head = 0
result.count = 0
result.transitions = newSeq[Transition](capacity)
# ── Core operations ───────────────────────────────────────────────────────────
proc add*(buf: var ReplayBuffer; t: Transition) =
buf.transitions[buf.head] = t
buf.head = (buf.head + 1) mod buf.capacity
if buf.count < buf.capacity:
inc buf.count
proc len*(buf: ReplayBuffer): int = buf.count
proc clear*(buf: var ReplayBuffer) =
## Drop all transitions (#48: opponent changed across battles).
## Old slots keep stale tensors until the ring overwrites them.
buf.head = 0
buf.count = 0
proc canSample*(buf: ReplayBuffer): bool =
buf.count >= buf.burnIn + buf.trainWindow
# ── Sampling ──────────────────────────────────────────────────────────────────
proc sampleSequences*(buf: ReplayBuffer; batchSize: int): seq[Sequence] =
## Sample `batchSize` contiguous sequences of length burnIn+trainWindow.
## Sequences never cross a done=true boundary and never wrap the ring buffer.
##
## Returns fewer than batchSize sequences if not enough valid starts exist.
## Returns empty seq if canSample is false.
if not buf.canSample: return @[]
let seqLen = buf.burnIn + buf.trainWindow
let oldest = if buf.count < buf.capacity: 0
else: buf.head # oldest valid index when full
# Build valid starting indices.
# ponytail: O(count) scan per sample call; upgrade to an indexed set of
# boundary positions if count reaches hundreds of thousands and profiling shows
# this is a bottleneck.
var validStarts: seq[int]
for i in 0 ..< buf.count - seqLen + 1:
# Absolute ring-buffer index for the i-th oldest transition
let startIdx = (oldest + i) mod buf.capacity
# Check: the sequence [startIdx .. startIdx+seqLen-2] must not contain done=true
# (a done at position k means the battle ended there; the next transition is
# from a new battle, so the sequence would cross a boundary).
# Also, the sequence must not wrap around the ring buffer.
let endIdx = startIdx + seqLen - 1 # exclusive of wrap check
if endIdx >= buf.capacity:
# Sequence wraps the ring buffer — invalid starting point.
continue
var crosses = false
for j in 0 ..< seqLen - 1:
if buf.transitions[startIdx + j].done:
crosses = true
break
if not crosses:
validStarts.add(startIdx)
if validStarts.len == 0: return @[]
result = newSeq[Sequence](min(batchSize, validStarts.len))
# Sample with replacement if batchSize > validStarts.len, else sample without.
# ponytail: sampling with replacement for simplicity; shuffle+take for
# without-replacement if the caller needs it.
for i in 0 ..< result.len:
let startIdx = validStarts[rand(validStarts.len - 1)]
var s: Sequence
s.burnIn = newSeq[Transition](buf.burnIn)
s.train = newSeq[Transition](buf.trainWindow)
for j in 0 ..< buf.burnIn:
s.burnIn[j] = buf.transitions[startIdx + j]
for j in 0 ..< buf.trainWindow:
s.train[j] = buf.transitions[startIdx + buf.burnIn + j]
result[i] = s
SAC_LSTM_Bot_garage/src/SAC_LSTM_Bot/rewards.nim
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## rewards.nim — Raw reward computation + running mean/variance normalizer.
## Welford online algorithm; safe cold-start (0 or 1 samples).
import std/math
# ── Lever-2 shaping constants (#59, campaign v2) — TUNABLE ────────────────────
# Scale discipline: commensurate with existing magnitudes (dealt p=1 was +4,
# wall tick -5/tick, win +20). Death/loss and win terms stay dominant; these
# only re-rank mid-band behaviors (fight vs outlive vs get-rammed).
const
# Multiplier on the bullet-damage-dealt term: p=1 hit +4 -> +5. Low-power
# spam stays unprofitable (6*0.1-2 = -1.4 < 0 even after x1.25).
AggressionMult* = 1.25 # ponytail: TUNABLE — raise toward 1.5 if v2 bot still passivity-leaning
# Flat per landed shot on top of damage: discrete accuracy signal.
HitBonus* = 0.5 # ponytail: TUNABLE — keep < 6p-2 at min viable power (~0.34)
# Per bot-bot collision (BotHitBotEvent): server deals RAM_DAMAGE=0.6 to
# both parties but only notifies the hitter — each receipt = damage taken.
RamTakenPenalty* = 3.0 # ponytail: TUNABLE — vs p=0.8 bullet received (-6.8)
# Enemy-charging deterrent: distance/diagonal below this => escalating
# negative (max at zero distance), suppressed while we deal damage that step.
ChargeDistFrac* = 0.12 # ponytail: TUNABLE — ~120u of 800x600 diag (1000)
ChargePenalty* = 2.0 # ponytail: TUNABLE — per-tick ceiling, milder than wall (-5/tick)
# ── Raw reward ────────────────────────────────────────────────────────────────
proc computeReward*(
damageInflicted: float64 = 0.0, # fire power p of own shot that hit
damageReceived: float64 = 0.0, # fire power p_e of enemy shot that hit
wallHitTicks: int = 0, # ticks in wall contact this step
wastedShotPower: float64 = 0.0, # fire power of shot that missed/hit wall
hitCount: int = 0, # own bullets that hit the enemy this step (#59)
ramTakenCount: int = 0, # collisions where we were the victim (#59)
enemyDistFrac: float64 = 2.0, # enemy dist / arena diag; >ChargeDistFrac when no contact (#59)
win: bool = false,
loss: bool = false
): float64 =
## Returns the raw (un-normalized) reward for one decision step.
## Damage formula: 4p + 2(p-1) = 6p - 2 (matches Tank Royale bullet rules).
let p = damageInflicted
let pe = damageReceived
if p > 0.0:
result += AggressionMult * (6.0 * p - 2.0)
if hitCount > 0: result += HitBonus * hitCount.float64
if pe > 0.0: result -= 6.0 * pe - 2.0
result -= RamTakenPenalty * ramTakenCount.float64
if enemyDistFrac < ChargeDistFrac and p <= 0.0:
result -= ChargePenalty * (1.0 - enemyDistFrac / ChargeDistFrac)
result -= 5.0 * wallHitTicks.float64
if wastedShotPower > 0.0: result -= 0.1 * wastedShotPower
if win: result += 20.0
if loss: result -= 10.0
# ── Running normalizer (Welford) ──────────────────────────────────────────────
const NormEps = 1e-8
type
RewardNormalizer* = object
n*: int # samples seen
mean*: float64
m2*: float64 # sum of squared deviations (Welford M2)
proc update*(rn: var RewardNormalizer; r: float64) =
rn.n += 1
let delta = r - rn.mean
rn.mean += delta / rn.n.float64
let delta2 = r - rn.mean
rn.m2 += delta * delta2
proc normalize*(rn: RewardNormalizer; r: float64): float64 =
## Returns (r - mean) / (std + eps) once statistics are meaningful
## (n >= 4 and spread well above zero). Before that, returns the RAW
## reward unchanged — Welford M2 collapses to exactly 0 when early raw
## rewards are identical, and dividing by the 1e-8 floor then z-scores
## the first differing reward to ~1e8, poisoning TD targets.
if rn.n < 4: return r
let variance = rn.m2 / rn.n.float64 # ponytail: population var; switch to n-1 if bias matters
let stddev = sqrt(variance)
if stddev <= 1e-3 * (abs(rn.mean) + 1.0): return r
# ponytail: warm-up pass-through ceiling — raw rewards bypass normalization
# until stats are meaningful; upgrade = persist Welford state in checkpoint
# if warm-up noise ever hurts learning.
result = (r - rn.mean) / (stddev + NormEps)
SAC_LSTM_Bot_garage/src/SAC_LSTM_Bot/state
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## State vector module — produces a 35-dimensional normalized tensor for SAC+LSTM policy.
## No bot API imports; takes plain data structs populated from game events.
## The LSTM handles temporal context, so no explicit history window here.
import std/math
import arraymancer
const STATE_DIM* = 35
type
BulletData* = object
## Enemy bullet in flight (absolute arena coords + fire power).
x*, y*: float64
power*: float64 # fire power in [0.1, 3.0]; speed = 20 - 3*power
EnemyData* = object
## Current enemy state, from the most recent onScannedBot event.
x*, y*: float64
direction*: float64
speed*: float64
energy*: float64
hasFired*: bool
lastFirePower*: float64
prevSpeed*: float64 # speed from the previous scan (for acceleration)
prevDirection*: float64 # direction from the previous scan (for turn rate)
hasPrevScan*: bool # true once we have at least two scans
GameState* = object
## Accumulates data from bot events. Populate fields before calling buildState.
# Own bot
x*, y*: float64
direction*: float64
speed*: float64
energy*: float64
gunDirection*: float64
gunHeat*: float64
arenaWidth*, arenaHeight*: float64
# Enemy
hasContact*: bool
enemy*: EnemyData
ticksSinceLastScan*: int
# Bullets in flight (up to 3 tracked)
bullets*: array[3, BulletData]
bulletCount*: int
proc buildState*(gs: GameState): Tensor[float32] =
## Build the 35-float normalized state tensor.
##
## Layout:
## [0-6] own bot: x/aW, y/aH, dir/360, speed/8, energy/100, gunDir/360, gunHeat/1.8
## [7-13] enemy: x/aW, y/aH, dir/360, speed/8, energy/100, hasFired, lastFirePower/3
## [14-17] derived: enemyAccel/8, enemyTurnRate/180, relBearing/180, distance/diag
## [18-21] walls: top, bottom, left, right — each / max(aW,aH)
## [22-33] bullets: up to 3 × (relX/aW, relY/aH, speed/20, ticksToImpact clamped to 1)
## [34] scan staleness: ticksSinceLastScan/30 clamped to 1
result = zeros[float32](STATE_DIM)
let aW = gs.arenaWidth
let aH = gs.arenaHeight
let diag = sqrt(aW * aW + aH * aH)
let wMax = max(aW, aH)
# --- Own bot (0-6) ---
result[0] = float32(gs.x / aW)
result[1] = float32(gs.y / aH)
result[2] = float32(gs.direction / 360.0)
result[3] = float32(gs.speed / 8.0)
result[4] = float32(gs.energy / 100.0)
result[5] = float32(gs.gunDirection / 360.0)
result[6] = float32(gs.gunHeat / 1.8)
# --- Enemy current (7-13) ---
if gs.hasContact:
result[7] = float32(gs.enemy.x / aW)
result[8] = float32(gs.enemy.y / aH)
result[9] = float32(gs.enemy.direction / 360.0)
result[10] = float32(gs.enemy.speed / 8.0)
result[11] = float32(gs.enemy.energy / 100.0)
result[12] = float32(if gs.enemy.hasFired: 1.0 else: 0.0)
result[13] = float32(gs.enemy.lastFirePower / 3.0)
# --- Derived (14-17) ---
if gs.hasContact:
if gs.enemy.hasPrevScan:
result[14] = float32((gs.enemy.speed - gs.enemy.prevSpeed) / 8.0)
let dDir = ((gs.enemy.direction - gs.enemy.prevDirection) + 540.0) mod 360.0 - 180.0
result[15] = float32(dDir / 180.0)
let dx = gs.enemy.x - gs.x
let dy = gs.enemy.y - gs.y
let absDir = (180.0 * arctan2(dx, dy) / PI + 360.0) mod 360.0
let relBearing = ((absDir - gs.direction) + 540.0) mod 360.0 - 180.0
result[16] = float32(relBearing / 180.0)
result[17] = float32(sqrt(dx * dx + dy * dy) / diag)
# --- Wall distances (18-21): top, bottom, left, right ---
result[18] = float32((aH - gs.y) / wMax)
result[19] = float32(gs.y / wMax)
result[20] = float32(gs.x / wMax)
result[21] = float32((aW - gs.x) / wMax)
# --- Bullet tracking (22-33): up to 3 bullets × 4 floats ---
# Per slot: relX/aW, relY/aH, speed/20, ticksToImpact/diag (clamped to 1)
for i in 0 ..< min(gs.bulletCount, 3):
let b = gs.bullets[i]
let bSpd = 20.0 - 3.0 * b.power
let bdx = b.x - gs.x
let bdy = b.y - gs.y
let bdist = sqrt(bdx * bdx + bdy * bdy)
let ticks = if bSpd > 0.0: min(bdist / bSpd / diag, 1.0) else: 0.0
let base = 22 + i * 4
result[base + 0] = float32(bdx / aW)
result[base + 1] = float32(bdy / aH)
result[base + 2] = float32(bSpd / 20.0)
result[base + 3] = float32(ticks)
# --- Scan staleness (34) ---
result[34] = float32(min(gs.ticksSinceLastScan.float64 / 30.0, 1.0))
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## training.nim — SAC-v2 update for the LSTM Actor + twin Critic.
## Manual backprop; no autograd. Uses Arraymancer tensors throughout.
##
## Config env vars:
## SACLSTM_LR_ACTOR (default: 3e-4)
## SACLSTM_LR_CRITIC (default: 3e-4)
## SACLSTM_LR_ALPHA (default: 3e-4)
## SACLSTM_GAMMA (default: 0.99)
## SACLSTM_TAU (default: 0.005)
## SACLSTM_TARGET_ENTROPY (default: -4.0)
import arraymancer except Linear
import std/[math, os, strutils]
import SAC_LSTM_Bot/network
import SAC_LSTM_Bot/weights
import SAC_LSTM_Bot/replay_buffer
# ── Config ────────────────────────────────────────────────────────────────────
proc getLrActor*(): float32 = parseFloat(getEnv("SACLSTM_LR_ACTOR", "3e-4")).float32
proc getLrCritic*(): float32 = parseFloat(getEnv("SACLSTM_LR_CRITIC", "3e-4")).float32
proc getLrAlpha*(): float32 = parseFloat(getEnv("SACLSTM_LR_ALPHA", "3e-4")).float32
proc getGamma*(): float32 = parseFloat(getEnv("SACLSTM_GAMMA", "0.99")).float32
proc getTau*(): float32 = parseFloat(getEnv("SACLSTM_TAU", "0.005")).float32
proc getTargetEntropy*(): float32 =
parseFloat(getEnv("SACLSTM_TARGET_ENTROPY", "-4.0")).float32
# ── SACTrainer ────────────────────────────────────────────────────────────────
type
SACTrainer* = object
actor*: ActorNet
critic1*: CriticNet
critic2*: CriticNet
targetCritic1*: CriticNet
targetCritic2*: CriticNet
logAlpha*: float32 ## log of entropy temperature; alpha = exp(logAlpha)
targetEntropy*: float32
tau*: float32
lrActor*: float32
lrCritic*: float32
lrAlpha*: float32
gamma*: float32
adam*: SACAdamStates
SACMetrics* = object
criticLoss*: float32
actorLoss*: float32
alphaLoss*: float32
alpha*: float32
proc initSACTrainer*(stateDim, actionDim: int): SACTrainer =
result.actor = initActorNet(stateDim)
result.critic1 = initCriticNet(stateDim, actionDim)
result.critic2 = initCriticNet(stateDim, actionDim)
result.targetCritic1 = result.critic1
result.targetCritic2 = result.critic2
result.logAlpha = 0.0'f32
result.targetEntropy = getTargetEntropy()
result.tau = getTau()
result.lrActor = getLrActor()
result.lrCritic = getLrCritic()
result.lrAlpha = getLrAlpha()
result.gamma = getGamma()
result.adam = initSACAdamStates(result.actor, result.critic1, result.critic2)
proc alpha*(t: SACTrainer): float32 = exp(t.logAlpha)
# ── Adam steps ────────────────────────────────────────────────────────────────
proc adamStepScalar(param: var float32; grad: float32;
state: var AdamVar; lr: float32) =
## Scalar Adam for logAlpha (state.m/v are shape [1] tensors).
inc state.t
let b1 = 0.9'f32; let b2 = 0.999'f32; let eps = 1e-8'f32
state.m[0] = b1 * state.m[0] + (1.0'f32 - b1) * grad
state.v[0] = b2 * state.v[0] + (1.0'f32 - b2) * grad * grad
let mHat = state.m[0] / (1.0'f32 - b1 ^ state.t.float32)
let vHat = state.v[0] / (1.0'f32 - b2 ^ state.t.float32)
param -= lr * mHat / (sqrt(vHat) + eps)
proc adamStepTensor(param: var Tensor[float32]; grad: Tensor[float32];
state: var AdamVar; lr: float32) =
## Tensor Adam (same pattern as PPO_Bot/training.nim adamStep).
inc state.t
let b1 = 0.9'f32; let b2 = 0.999'f32; let eps = 1e-8'f32
state.m = b1 *. state.m + (1.0'f32 - b1) *. grad
state.v = b2 *. state.v + (1.0'f32 - b2) *. (grad *. grad)
let mHat = state.m /. (1.0'f32 - b1 ^ state.t.float32)
let vHat = state.v /. (1.0'f32 - b2 ^ state.t.float32)
param -= lr *. mHat /. vHat.map(proc(x: float32): float32 = sqrt(x) + eps)
# ── Gradient clipping ─────────────────────────────────────────────────────────
proc globalNorm(grads: seq[Tensor[float32]]): float32 =
var sumSq = 0.0'f32
for g in grads:
for v in g: sumSq += v * v
sqrt(sumSq)
proc clipGrads(grads: var seq[Tensor[float32]]; maxNorm: float32) =
let norm = globalNorm(grads)
if norm > maxNorm and norm == norm:
let scale = maxNorm / norm
for g in grads.mitems: g = g *. scale
# ── Forward caches (for backprop) ─────────────────────────────────────────────
type
LinearFwd = object
inp, pre, act: Tensor[float32] # input, pre-relu, post-relu (or linear)
proc linearReluFwd(l: Linear; x: Tensor[float32]): LinearFwd =
result.inp = x
result.pre = l.w * x + l.b
result.act = relu(result.pre)
proc linearFwd(l: Linear; x: Tensor[float32]): LinearFwd =
result.inp = x
result.pre = l.w * x + l.b
result.act = result.pre # no nonlinearity
type
LSTMFwdCache = object
xh, gatesPre: Tensor[float32] # [inputDim+hd], [4*hd]
iGate, fGate, gGate, oGate: Tensor[float32] # [hd] each
cPrev, cPrime, hPrime: Tensor[float32] # [hd] each
proc lstmStepCached(cell: LSTMCell; x, h, c: Tensor[float32]): LSTMFwdCache =
result.cPrev = c
result.xh = concat(x, h, axis = 0)
result.gatesPre = cell.wCombined * result.xh + cell.bCombined
let hd = cell.hiddenDim
result.iGate = sigmoid(result.gatesPre[0 ..< hd])
result.fGate = sigmoid(result.gatesPre[hd ..< 2*hd])
result.gGate = tanhT(result.gatesPre[2*hd ..< 3*hd])
result.oGate = sigmoid(result.gatesPre[3*hd ..< 4*hd])
result.cPrime = result.fGate *. c + result.iGate *. result.gGate
result.hPrime = result.oGate *. tanhT(result.cPrime)
# ── Backward helpers ──────────────────────────────────────────────────────────
proc reluGrad(pre, dAct: Tensor[float32]): Tensor[float32] =
result = newTensor[float32](dAct.shape)
for i in 0 ..< dAct.shape[0]:
result[i] = if pre[i] > 0.0'f32: dAct[i] else: 0.0'f32
## Linear layer backward: returns (dx, dw, db) given upstream grad dAct.
## If hasRelu, applies relu' gate before computing gradients.
proc linearBack(w: Tensor[float32]; fwd: LinearFwd;
dAct: Tensor[float32]; hasRelu: bool):
tuple[dx, dw, db: Tensor[float32]] =
let dPre = if hasRelu: reluGrad(fwd.pre, dAct) else: dAct
result.dw = dPre.unsqueeze(1) * fwd.inp.unsqueeze(0) # [out, in]
result.db = dPre
result.dx = w.transpose * dPre # [in]
## LSTM single-step backward. dHPrime: [hd], dCPrime: [hd] (use zeros for truncated BPTT).
## Returns (dwCombined, dbCombined, dxh).
proc lstmBack(cell: LSTMCell; cache: LSTMFwdCache;
dHPrime, dCPrime: Tensor[float32]):
tuple[dwCombined, dbCombined, dxh: Tensor[float32]] =
let hd = cell.hiddenDim
let tanhCPrime = tanhT(cache.cPrime)
# Output gate
let dOGate_post = dHPrime *. tanhCPrime
# Cell state: gradient from h' and from downstream dCPrime
let dCPrimeTotal = dHPrime *. cache.oGate *.
(ones[float32](hd) - tanhCPrime *. tanhCPrime) + dCPrime
# Gate post-activation gradients
let dFGate_post = dCPrimeTotal *. cache.cPrev
let dIGate_post = dCPrimeTotal *. cache.gGate
let dGGate_post = dCPrimeTotal *. cache.iGate
# Gate pre-activation gradients (sigmoid', tanh')
let dIPre = dIGate_post *. cache.iGate *. (ones[float32](hd) - cache.iGate)
let dFPre = dFGate_post *. cache.fGate *. (ones[float32](hd) - cache.fGate)
let dGPre = dGGate_post *. (ones[float32](hd) - cache.gGate *. cache.gGate)
let dOPre = dOGate_post *. cache.oGate *. (ones[float32](hd) - cache.oGate)
# Concatenated gate gradient [4*hd]
let dGatesPre = concat(dIPre, dFPre, dGPre, dOPre, axis = 0)
result.dwCombined = dGatesPre.unsqueeze(1) * cache.xh.unsqueeze(0)
result.dbCombined = dGatesPre
result.dxh = cell.wCombined.transpose * dGatesPre
# ── Squashed-Gaussian log-prob and its gradients ──────────────────────────────
const
LOG_PROB_EPS = 1e-6'f32
LOG_STD_MIN = -5.0'f32
LOG_STD_MAX = 2.0'f32
## Given stored mu, clamped logStd, and sampled action = tanh(z), recover
## log π(a|s) and gradients w.r.t. mu and logStd.
proc squashedLogProb(mu, logStd, action: Tensor[float32]):
tuple[logProb: float32;
dLogProbDMu, dLogProbDLogStd: Tensor[float32]] =
let std = logStd.map(proc(v: float32): float32 = exp(v))
let z = mu # deterministic reparam: action = tanh(mu), so z ≡ mu, diff ≡ 0
result.dLogProbDMu = newTensor[float32](4)
result.dLogProbDLogStd = newTensor[float32](4)
let twoPiLog = 0.5'f32 * ln(2.0'f32 * PI.float32)
var lp = 0.0'f32
for i in 0 ..< 4:
let diff = (z[i] - mu[i]) / std[i]
let logNorm = -0.5'f32 * diff * diff - ln(std[i]) - twoPiLog
let tanhCorr = ln(1.0'f32 - action[i] * action[i] + LOG_PROB_EPS)
lp += logNorm - tanhCorr
result.dLogProbDMu[i] = diff / std[i] # (z-mu)/std²
result.dLogProbDLogStd[i] = diff * diff - 1.0'f32 # d logN / d logStd
result.logProb = lp
# ── Critic forward with activation cache ──────────────────────────────────────
type
CriticFwdCache = object
fc1: LinearFwd
lstm: LSTMFwdCache
fc2: LinearFwd
fc3: LinearFwd
q: float32
proc criticFwdCached(net: CriticNet; stateAction: Tensor[float32];
h, c: Tensor[float32]): CriticFwdCache =
result.fc1 = linearReluFwd(net.fc1, stateAction)
result.lstm = lstmStepCached(net.lstm, result.fc1.act, h, c)
result.fc2 = linearReluFwd(net.fc2, result.lstm.hPrime)
result.fc3 = linearFwd(net.fc3, result.fc2.act)
result.q = result.fc3.act[0]
# ── Critic backward ───────────────────────────────────────────────────────────
type
CriticGrads = object
dFc1W, dFc1B: Tensor[float32]
dFc2W, dFc2B: Tensor[float32]
dFc3W, dFc3B: Tensor[float32]
dLstmW, dLstmB: Tensor[float32]
dInput: Tensor[float32] ## grad w.r.t. stateAction input
proc criticBack(net: CriticNet; cache: CriticFwdCache; dQ: float32): CriticGrads =
let dFc3Act = [dQ].toTensor()
let fc3b = linearBack(net.fc3.w, cache.fc3, dFc3Act, hasRelu = false)
result.dFc3W = fc3b.dw; result.dFc3B = fc3b.db
let fc2b = linearBack(net.fc2.w, cache.fc2, fc3b.dx, hasRelu = true)
result.dFc2W = fc2b.dw; result.dFc2B = fc2b.db
let zeros_hd = zeros[float32](net.hiddenDim)
let lstmb = lstmBack(net.lstm, cache.lstm, fc2b.dx, zeros_hd)
result.dLstmW = lstmb.dwCombined; result.dLstmB = lstmb.dbCombined
# xh = [fc1.act | h_prev], dx is the x-part (fc1 output dim = hiddenDim)
let dLstmX = lstmb.dxh[0 ..< cache.fc1.act.shape[0]]
let fc1b = linearBack(net.fc1.w, cache.fc1, dLstmX, hasRelu = true)
result.dFc1W = fc1b.dw; result.dFc1B = fc1b.db
result.dInput = fc1b.dx # [stateDim + actionDim]
# ── Actor forward with activation cache ───────────────────────────────────────
type
ActorFwdCache = object
fc1: LinearFwd
lstm: LSTMFwdCache
fc2: LinearFwd
muHead: LinearFwd
lsHead: LinearFwd ## logStd head
mu: Tensor[float32] ## [4]
logStd: Tensor[float32] ## [4] clamped
action: Tensor[float32] ## [4] tanh(mu) — deterministic for gradient
proc actorFwdCached(net: ActorNet; state: Tensor[float32];
h, c: Tensor[float32]): ActorFwdCache =
result.fc1 = linearReluFwd(net.fc1, state)
result.lstm = lstmStepCached(net.lstm, result.fc1.act, h, c)
result.fc2 = linearReluFwd(net.fc2, result.lstm.hPrime)
result.muHead = linearFwd(net.muHead, result.fc2.act)
result.lsHead = linearFwd(net.logStdHead, result.fc2.act)
result.mu = result.muHead.act
result.logStd = result.lsHead.act.map(
proc(v: float32): float32 = clamp(v, LOG_STD_MIN, LOG_STD_MAX))
# Use tanh(mu) as the action for gradient computation (reparameterization).
# ponytail: deterministic here; add stochastic sample if off-policy bias matters.
result.action = tanhT(result.mu)
# ── Actor backward ────────────────────────────────────────────────────────────
type
ActorGrads = object
dFc1W, dFc1B: Tensor[float32]
dFc2W, dFc2B: Tensor[float32]
dMuW, dMuB: Tensor[float32]
dLogStdW, dLogStdB: Tensor[float32]
dLstmW, dLstmB: Tensor[float32]
proc actorBack(net: ActorNet; cache: ActorFwdCache;
dMu, dLogStd: Tensor[float32]): ActorGrads =
let muBack = linearBack(net.muHead.w, cache.muHead, dMu, hasRelu = false)
result.dMuW = muBack.dw; result.dMuB = muBack.db
let lsBack = linearBack(net.logStdHead.w, cache.lsHead, dLogStd, hasRelu = false)
result.dLogStdW = lsBack.dw; result.dLogStdB = lsBack.db
# fc2 gets grads from both output heads
let fc2b = linearBack(net.fc2.w, cache.fc2, muBack.dx + lsBack.dx, hasRelu = true)
result.dFc2W = fc2b.dw; result.dFc2B = fc2b.db
let zeros_hd = zeros[float32](net.hiddenDim)
let lstmb = lstmBack(net.lstm, cache.lstm, fc2b.dx, zeros_hd)
result.dLstmW = lstmb.dwCombined; result.dLstmB = lstmb.dbCombined
let dLstmX = lstmb.dxh[0 ..< cache.fc1.act.shape[0]]
let fc1b = linearBack(net.fc1.w, cache.fc1, dLstmX, hasRelu = true)
result.dFc1W = fc1b.dw; result.dFc1B = fc1b.db
# ── Adam application ──────────────────────────────────────────────────────────
proc applyActorAdam(net: var ActorNet; g: ActorGrads;
adam: var ActorAdam; lr: float32) =
adamStepTensor(net.fc1.w, g.dFc1W, adam.fc1.w, lr)
adamStepTensor(net.fc1.b, g.dFc1B, adam.fc1.b, lr)
adamStepTensor(net.fc2.w, g.dFc2W, adam.fc2.w, lr)
adamStepTensor(net.fc2.b, g.dFc2B, adam.fc2.b, lr)
adamStepTensor(net.muHead.w, g.dMuW, adam.muHead.w, lr)
adamStepTensor(net.muHead.b, g.dMuB, adam.muHead.b, lr)
adamStepTensor(net.logStdHead.w, g.dLogStdW, adam.logStdHead.w, lr)
adamStepTensor(net.logStdHead.b, g.dLogStdB, adam.logStdHead.b, lr)
adamStepTensor(net.lstm.wCombined, g.dLstmW, adam.lstm.wCombined, lr)
adamStepTensor(net.lstm.bCombined, g.dLstmB, adam.lstm.bCombined, lr)
proc applyCriticAdam(net: var CriticNet; g: CriticGrads;
adam: var CriticAdam; lr: float32) =
adamStepTensor(net.fc1.w, g.dFc1W, adam.fc1.w, lr)
adamStepTensor(net.fc1.b, g.dFc1B, adam.fc1.b, lr)
adamStepTensor(net.fc2.w, g.dFc2W, adam.fc2.w, lr)
adamStepTensor(net.fc2.b, g.dFc2B, adam.fc2.b, lr)
adamStepTensor(net.fc3.w, g.dFc3W, adam.fc3.w, lr)
adamStepTensor(net.fc3.b, g.dFc3B, adam.fc3.b, lr)
adamStepTensor(net.lstm.wCombined, g.dLstmW, adam.lstm.wCombined, lr)
adamStepTensor(net.lstm.bCombined, g.dLstmB, adam.lstm.bCombined, lr)
# ── Soft target update ────────────────────────────────────────────────────────
proc softUpdateLinear(target: var Linear; src: Linear; tau: float32) =
target.w = tau *. src.w + (1.0'f32 - tau) *. target.w
target.b = tau *. src.b + (1.0'f32 - tau) *. target.b
proc softUpdateLSTM(target: var LSTMCell; src: LSTMCell; tau: float32) =
target.wCombined = tau *. src.wCombined + (1.0'f32 - tau) *. target.wCombined
target.bCombined = tau *. src.bCombined + (1.0'f32 - tau) *. target.bCombined
proc softUpdateCritic(target: var CriticNet; src: CriticNet; tau: float32) =
softUpdateLinear(target.fc1, src.fc1, tau)
softUpdateLSTM(target.lstm, src.lstm, tau)
softUpdateLinear(target.fc2, src.fc2, tau)
softUpdateLinear(target.fc3, src.fc3, tau)
# ── Gradient accumulators ─────────────────────────────────────────────────────
proc zeroCriticGrads(net: CriticNet): CriticGrads =
result.dFc1W = zeros[float32](net.fc1.w.shape)
result.dFc1B = zeros[float32](net.fc1.b.shape)
result.dFc2W = zeros[float32](net.fc2.w.shape)
result.dFc2B = zeros[float32](net.fc2.b.shape)
result.dFc3W = zeros[float32](net.fc3.w.shape)
result.dFc3B = zeros[float32](net.fc3.b.shape)
result.dLstmW = zeros[float32](net.lstm.wCombined.shape)
result.dLstmB = zeros[float32](net.lstm.bCombined.shape)
result.dInput = zeros[float32](net.fc1.w.shape[1]) # [stateDim+actionDim]
proc zeroActorGrads(net: ActorNet): ActorGrads =
result.dFc1W = zeros[float32](net.fc1.w.shape)
result.dFc1B = zeros[float32](net.fc1.b.shape)
result.dFc2W = zeros[float32](net.fc2.w.shape)
result.dFc2B = zeros[float32](net.fc2.b.shape)
result.dMuW = zeros[float32](net.muHead.w.shape)
result.dMuB = zeros[float32](net.muHead.b.shape)
result.dLogStdW = zeros[float32](net.logStdHead.w.shape)
result.dLogStdB = zeros[float32](net.logStdHead.b.shape)
result.dLstmW = zeros[float32](net.lstm.wCombined.shape)
result.dLstmB = zeros[float32](net.lstm.bCombined.shape)
proc addCriticGrads(a: var CriticGrads; b: CriticGrads) =
a.dFc1W += b.dFc1W; a.dFc1B += b.dFc1B
a.dFc2W += b.dFc2W; a.dFc2B += b.dFc2B
a.dFc3W += b.dFc3W; a.dFc3B += b.dFc3B
a.dLstmW += b.dLstmW; a.dLstmB += b.dLstmB
# dInput not accumulated (not used for parameter update)
proc addActorGrads(a: var ActorGrads; b: ActorGrads) =
a.dFc1W += b.dFc1W; a.dFc1B += b.dFc1B
a.dFc2W += b.dFc2W; a.dFc2B += b.dFc2B
a.dMuW += b.dMuW; a.dMuB += b.dMuB
a.dLogStdW += b.dLogStdW; a.dLogStdB += b.dLogStdB
a.dLstmW += b.dLstmW; a.dLstmB += b.dLstmB
proc scaleCriticGrads(g: var CriticGrads; s: float32) =
g.dFc1W = g.dFc1W *. s; g.dFc1B = g.dFc1B *. s
g.dFc2W = g.dFc2W *. s; g.dFc2B = g.dFc2B *. s
g.dFc3W = g.dFc3W *. s; g.dFc3B = g.dFc3B *. s
g.dLstmW = g.dLstmW *. s; g.dLstmB = g.dLstmB *. s
proc scaleActorGrads(g: var ActorGrads; s: float32) =
g.dFc1W = g.dFc1W *. s; g.dFc1B = g.dFc1B *. s
g.dFc2W = g.dFc2W *. s; g.dFc2B = g.dFc2B *. s
g.dMuW = g.dMuW *. s; g.dMuB = g.dMuB *. s
g.dLogStdW = g.dLogStdW *. s; g.dLogStdB = g.dLogStdB *. s
g.dLstmW = g.dLstmW *. s; g.dLstmB = g.dLstmB *. s
proc criticGradsAsSeq(g: CriticGrads): seq[Tensor[float32]] =
@[g.dFc1W, g.dFc1B, g.dFc2W, g.dFc2B, g.dFc3W, g.dFc3B, g.dLstmW, g.dLstmB]
proc applyClipToCritic(g: var CriticGrads; maxNorm: float32) =
var gs = criticGradsAsSeq(g)
clipGrads(gs, maxNorm)
g.dFc1W = gs[0]; g.dFc1B = gs[1]
g.dFc2W = gs[2]; g.dFc2B = gs[3]
g.dFc3W = gs[4]; g.dFc3B = gs[5]
g.dLstmW = gs[6]; g.dLstmB = gs[7]
proc applyClipToActor(g: var ActorGrads; maxNorm: float32) =
var gs = @[g.dFc1W, g.dFc1B, g.dFc2W, g.dFc2B,
g.dMuW, g.dMuB, g.dLogStdW, g.dLogStdB, g.dLstmW, g.dLstmB]
clipGrads(gs, maxNorm)
g.dFc1W = gs[0]; g.dFc1B = gs[1]
g.dFc2W = gs[2]; g.dFc2B = gs[3]
g.dMuW = gs[4]; g.dMuB = gs[5]
g.dLogStdW = gs[6]; g.dLogStdB = gs[7]
g.dLstmW = gs[8]; g.dLstmB = gs[9]
# ── SAC update ────────────────────────────────────────────────────────────────
proc sacUpdate*(trainer: var SACTrainer; sequences: seq[Sequence]): SACMetrics =
## One SAC-v2 update given a batch of sequences. No-op if empty.
if sequences.len == 0: return
let N = sequences.len.float32
let alph = trainer.alpha()
let gamma = trainer.gamma
var totalCriticLoss = 0.0'f32
var totalActorLoss = 0.0'f32
var totalAlphaLoss = 0.0'f32
var accC1Grads = zeroCriticGrads(trainer.critic1)
var accC2Grads = zeroCriticGrads(trainer.critic2)
var accAGrads = zeroActorGrads(trainer.actor)
var dLogAlpha = 0.0'f32
for sq in sequences:
# ── 1. Burn-in: warm up hidden states, no gradient ──────────────────────
var actorH = zeros[float32](trainer.actor.hiddenDim)
var actorC = zeros[float32](trainer.actor.hiddenDim)
var c1H = zeros[float32](trainer.critic1.hiddenDim)
var c1C = zeros[float32](trainer.critic1.hiddenDim)
var c2H = zeros[float32](trainer.critic2.hiddenDim)
var c2C = zeros[float32](trainer.critic2.hiddenDim)
var tc1H = zeros[float32](trainer.targetCritic1.hiddenDim)
var tc1C = zeros[float32](trainer.targetCritic1.hiddenDim)
var tc2H = zeros[float32](trainer.targetCritic2.hiddenDim)
var tc2C = zeros[float32](trainer.targetCritic2.hiddenDim)
for tr in sq.burnIn:
let sa = concat(tr.state, tr.action, axis = 0)
let af = lstmStepCached(trainer.actor.lstm,
relu(trainer.actor.fc1.linear(tr.state)), actorH, actorC)
actorH = af.hPrime; actorC = af.cPrime
let c1f = lstmStepCached(trainer.critic1.lstm,
relu(trainer.critic1.fc1.linear(sa)), c1H, c1C)
c1H = c1f.hPrime; c1C = c1f.cPrime
let c2f = lstmStepCached(trainer.critic2.lstm,
relu(trainer.critic2.fc1.linear(sa)), c2H, c2C)
c2H = c2f.hPrime; c2C = c2f.cPrime
let tc1f = lstmStepCached(trainer.targetCritic1.lstm,
relu(trainer.targetCritic1.fc1.linear(sa)), tc1H, tc1C)
tc1H = tc1f.hPrime; tc1C = tc1f.cPrime
let tc2f = lstmStepCached(trainer.targetCritic2.lstm,
relu(trainer.targetCritic2.fc1.linear(sa)), tc2H, tc2C)
tc2H = tc2f.hPrime; tc2C = tc2f.cPrime
# ── 2–4. Training window ─────────────────────────────────────────────────
let T = sq.train.len.float32
var seqC1Grads = zeroCriticGrads(trainer.critic1)
var seqC2Grads = zeroCriticGrads(trainer.critic2)
var seqAGrads = zeroActorGrads(trainer.actor)
var seqDLogAlpha = 0.0'f32
for tr in sq.train:
let s = tr.state
let a = tr.action
let r = tr.reward
let sn = tr.nextState
let d = if tr.done: 0.0'f32 else: 1.0'f32
let sa = concat(s, a, axis = 0)
# ── 2. Critic update ─────────────────────────────────────────────────
let c1Cache = criticFwdCached(trainer.critic1, sa, c1H, c1C)
let c2Cache = criticFwdCached(trainer.critic2, sa, c2H, c2C)
# ── 3. Actor update (run first to get actorFwd on s before advancing h/c) ─
let actorFwd = actorFwdCached(trainer.actor, s, actorH, actorC)
let aCurr = actorFwd.action
let lpResult = squashedLogProb(actorFwd.mu, actorFwd.logStd, aCurr)
let logProbA = lpResult.logProb
# Advance actor hidden state from s → sn before computing actorNxt
actorH = actorFwd.lstm.hPrime; actorC = actorFwd.lstm.cPrime
# Next-state action from current actor (uses h/c advanced through s)
let actorNxt = actorFwdCached(trainer.actor, sn, actorH, actorC)
let aN = actorNxt.action
let lpN = squashedLogProb(actorNxt.mu, actorNxt.logStd, aN).logProb
let saN = concat(sn, aN, axis = 0)
# Target Q
let tc1Cache = criticFwdCached(trainer.targetCritic1, saN, tc1H, tc1C)
let tc2Cache = criticFwdCached(trainer.targetCritic2, saN, tc2H, tc2C)
let minQTarg = min(tc1Cache.q, tc2Cache.q)
# Bellman target
let y = r + gamma * d * (minQTarg - alph * lpN)
let errQ1 = c1Cache.q - y
let errQ2 = c2Cache.q - y
totalCriticLoss += 0.5'f32 * (errQ1 * errQ1 + errQ2 * errQ2)
# MSE gradient: d_loss/d_q = (q - y) [scaling applied at accumulation]
addCriticGrads(seqC1Grads, criticBack(trainer.critic1, c1Cache, errQ1))
addCriticGrads(seqC2Grads, criticBack(trainer.critic2, c2Cache, errQ2))
# Advance critic hidden states
c1H = c1Cache.lstm.hPrime; c1C = c1Cache.lstm.cPrime
c2H = c2Cache.lstm.hPrime; c2C = c2Cache.lstm.cPrime
tc1H = tc1Cache.lstm.hPrime; tc1C = tc1Cache.lstm.cPrime
tc2H = tc2Cache.lstm.hPrime; tc2C = tc2Cache.lstm.cPrime
# ── 3 (cont). Actor gradient via critic ─────────────────────────────────
# Q-values for current policy action (critics used as frozen estimators)
let saCurr = concat(s, aCurr, axis = 0)
let qA1Cache = criticFwdCached(trainer.critic1, saCurr, c1H, c1C)
let qA2Cache = criticFwdCached(trainer.critic2, saCurr, c2H, c2C)
let q1Val = qA1Cache.q
let q2Val = qA2Cache.q
let qA1Back = criticBack(trainer.critic1, qA1Cache, -1.0'f32)
let qA2Back = criticBack(trainer.critic2, qA2Cache, -1.0'f32)
totalActorLoss += alph * logProbA - min(q1Val, q2Val)
# Gradient of -minQ w.r.t. action = dInput[stateDim ..< stateDim+actionDim]
# from the critic whose Q was smaller.
let minQBack = if q1Val <= q2Val: qA1Back else: qA2Back
let stateDim = s.shape[0]
let actionDim = aCurr.shape[0]
let dQdA = minQBack.dInput[stateDim ..< stateDim + actionDim]
# Chain through tanh: d(tanh(mu))/d(mu) = 1 - action²
let dTanh = aCurr.map(proc(a: float32): float32 = 1.0'f32 - a * a)
# Total gradient w.r.t. mu: (alpha * dLogP/dMu + dQ/dA) * dTanh/dMu
let dMu = (alph *. lpResult.dLogProbDMu + dQdA) *. dTanh
let dLogStd = alph *. lpResult.dLogProbDLogStd
addActorGrads(seqAGrads, actorBack(trainer.actor, actorFwd, dMu, dLogStd))
# ── 4. Alpha update ──────────────────────────────────────────────────
# Loss = -log_alpha * stop_grad(logProb + targetEntropy)
# d_loss/d_log_alpha = -(logProb + targetEntropy)
totalAlphaLoss += -trainer.logAlpha * (logProbA + trainer.targetEntropy)
seqDLogAlpha += -(logProbA + trainer.targetEntropy)
# Average sequence grads over T steps, accumulate over batch
scaleCriticGrads(seqC1Grads, 1.0'f32 / T)
scaleCriticGrads(seqC2Grads, 1.0'f32 / T)
scaleActorGrads(seqAGrads, 1.0'f32 / T)
addCriticGrads(accC1Grads, seqC1Grads)
addCriticGrads(accC2Grads, seqC2Grads)
addActorGrads(accAGrads, seqAGrads)
dLogAlpha += seqDLogAlpha / T
# Average over batch
scaleCriticGrads(accC1Grads, 1.0'f32 / N)
scaleCriticGrads(accC2Grads, 1.0'f32 / N)
scaleActorGrads(accAGrads, 1.0'f32 / N)
dLogAlpha /= N
# Gradient clipping (max_norm = 1.0)
applyClipToCritic(accC1Grads, 1.0'f32)
applyClipToCritic(accC2Grads, 1.0'f32)
applyClipToActor(accAGrads, 1.0'f32)
# Apply Adam updates
applyCriticAdam(trainer.critic1, accC1Grads, trainer.adam.critic1, trainer.lrCritic)
applyCriticAdam(trainer.critic2, accC2Grads, trainer.adam.critic2, trainer.lrCritic)
applyActorAdam(trainer.actor, accAGrads, trainer.adam.actor, trainer.lrActor)
adamStepScalar(trainer.logAlpha, dLogAlpha, trainer.adam.alpha, trainer.lrAlpha)
# ── 5. Soft target update ──────────────────────────────────────────────────
softUpdateCritic(trainer.targetCritic1, trainer.critic1, trainer.tau)
softUpdateCritic(trainer.targetCritic2, trainer.critic2, trainer.tau)
let totalSteps = N * sequences[0].train.len.float32
result.criticLoss = totalCriticLoss / totalSteps
result.actorLoss = totalActorLoss / totalSteps
result.alphaLoss = totalAlphaLoss / totalSteps
result.alpha = trainer.alpha()
SAC_LSTM_Bot_garage/src/SAC_LSTM_Bot/weights.nim
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## weights.nim — save/load all SAC-LSTM network tensors as .npy inside a .zip.
##
## Strategy: write_npy writes to paths; zip/zipfiles.addFile reads from paths.
## So we write each tensor to a temp .npy, add it to the zip, then delete temps.
## Load reverses: extract each entry to a temp .npy, read_npy, delete.
## Atomic save: build the zip in a temp path, then rename over the target.
import arraymancer except Linear
import zip/zipfiles
import std/[os, times, strutils]
import SAC_LSTM_Bot/network
# ── Adam state types (used by training.nim) ───────────────────────────────────
type
AdamVar* = object
m*, v*: Tensor[float32]
t*: int
## Adam states for one Linear layer (w and b).
LinearAdam* = object
w*, b*: AdamVar
## Adam states for one LSTMCell (wCombined and bCombined).
LSTMCellAdam* = object
wCombined*, bCombined*: AdamVar
## Adam states for one ActorNet.
ActorAdam* = object
fc1*, fc2*, muHead*, logStdHead*: LinearAdam
lstm*: LSTMCellAdam
## Adam states for one CriticNet.
CriticAdam* = object
fc1*, fc2*, fc3*: LinearAdam
lstm*: LSTMCellAdam
SACAdamStates* = object
actor*: ActorAdam
critic1*: CriticAdam
critic2*: CriticAdam
alpha*: AdamVar # scalar, shape [1]
initialized*: bool
# ── Init helpers ──────────────────────────────────────────────────────────────
proc initAdamVar(t: Tensor[float32]): AdamVar =
AdamVar(m: zeros[float32](t.shape), v: zeros[float32](t.shape), t: 0)
proc initLinearAdam*(l: Linear): LinearAdam =
LinearAdam(w: initAdamVar(l.w), b: initAdamVar(l.b))
proc initLSTMCellAdam*(c: LSTMCell): LSTMCellAdam =
LSTMCellAdam(
wCombined: initAdamVar(c.wCombined),
bCombined: initAdamVar(c.bCombined))
proc initActorAdam*(a: ActorNet): ActorAdam =
ActorAdam(
fc1: initLinearAdam(a.fc1),
fc2: initLinearAdam(a.fc2),
muHead: initLinearAdam(a.muHead),
logStdHead: initLinearAdam(a.logStdHead),
lstm: initLSTMCellAdam(a.lstm))
proc initCriticAdam*(c: CriticNet): CriticAdam =
CriticAdam(
fc1: initLinearAdam(c.fc1),
fc2: initLinearAdam(c.fc2),
fc3: initLinearAdam(c.fc3),
lstm: initLSTMCellAdam(c.lstm))
proc initSACAdamStates*(actor: ActorNet; critic1, critic2: CriticNet): SACAdamStates =
result.actor = initActorAdam(actor)
result.critic1 = initCriticAdam(critic1)
result.critic2 = initCriticAdam(critic2)
result.alpha = initAdamVar(ones[float32](1))
result.initialized = true
# ── Internal: temp dir per save ───────────────────────────────────────────────
proc tmpDir(): string =
getTempDir() / ("sacw_" & $int(epochTime() * 1000))
# ── Save helpers ──────────────────────────────────────────────────────────────
template addT(z: var ZipArchive; name: string; t: Tensor[float32]; tmp: string) =
## Write tensor to a temp file, add to zip, delete temp file.
let p = tmp / name
t.write_npy(p)
z.addFile(name, p)
proc addLinear(z: var ZipArchive; prefix: string; l: Linear; tmp: string) =
addT(z, prefix & "_w.npy", l.w, tmp)
addT(z, prefix & "_b.npy", l.b, tmp)
proc addLSTMCell(z: var ZipArchive; prefix: string; c: LSTMCell; tmp: string) =
addT(z, prefix & "_wc.npy", c.wCombined, tmp)
addT(z, prefix & "_bc.npy", c.bCombined, tmp)
proc addActorNet(z: var ZipArchive; prefix: string; a: ActorNet; tmp: string) =
addLinear(z, prefix & "_fc1", a.fc1, tmp)
addLSTMCell(z, prefix & "_lstm", a.lstm, tmp)
addLinear(z, prefix & "_fc2", a.fc2, tmp)
addLinear(z, prefix & "_mu", a.muHead, tmp)
addLinear(z, prefix & "_logstd", a.logStdHead, tmp)
proc addCriticNet(z: var ZipArchive; prefix: string; c: CriticNet; tmp: string) =
addLinear(z, prefix & "_fc1", c.fc1, tmp)
addLSTMCell(z, prefix & "_lstm", c.lstm, tmp)
addLinear(z, prefix & "_fc2", c.fc2, tmp)
addLinear(z, prefix & "_fc3", c.fc3, tmp)
proc addAdamVar(z: var ZipArchive; prefix: string; v: AdamVar; tmp: string) =
addT(z, prefix & "_m.npy", v.m, tmp)
addT(z, prefix & "_v.npy", v.v, tmp)
proc addLinearAdam(z: var ZipArchive; prefix: string; la: LinearAdam; tmp: string) =
addAdamVar(z, prefix & "_w", la.w, tmp)
addAdamVar(z, prefix & "_b", la.b, tmp)
proc addLSTMCellAdam(z: var ZipArchive; prefix: string; la: LSTMCellAdam; tmp: string) =
addAdamVar(z, prefix & "_wc", la.wCombined, tmp)
addAdamVar(z, prefix & "_bc", la.bCombined, tmp)
proc addActorAdam(z: var ZipArchive; prefix: string; a: ActorAdam; tmp: string) =
addLinearAdam(z, prefix & "_fc1", a.fc1, tmp)
addLSTMCellAdam(z, prefix & "_lstm", a.lstm, tmp)
addLinearAdam(z, prefix & "_fc2", a.fc2, tmp)
addLinearAdam(z, prefix & "_mu", a.muHead, tmp)
addLinearAdam(z, prefix & "_logstd", a.logStdHead, tmp)
proc addCriticAdam(z: var ZipArchive; prefix: string; c: CriticAdam; tmp: string) =
addLinearAdam(z, prefix & "_fc1", c.fc1, tmp)
addLSTMCellAdam(z, prefix & "_lstm", c.lstm, tmp)
addLinearAdam(z, prefix & "_fc2", c.fc2, tmp)
addLinearAdam(z, prefix & "_fc3", c.fc3, tmp)
# ── Public API ────────────────────────────────────────────────────────────────
proc saveWeights*(path: string;
actor: ActorNet;
critic1, critic2: CriticNet;
targetCritic1, targetCritic2: CriticNet;
alpha: float32) =
## Save network tensors (no Adam states) to `path` (.zip).
## Atomic: writes to a temp path first, then renames.
let tmp = tmpDir()
createDir(tmp)
let tmpZip = path & ".tmp"
try:
var z: ZipArchive
if not z.open(tmpZip, fmWrite):
raise newException(IOError, "cannot create zip: " & tmpZip)
addActorNet(z, "actor", actor, tmp)
addCriticNet(z, "c1", critic1, tmp)
addCriticNet(z, "c2", critic2, tmp)
addCriticNet(z, "tc1", targetCritic1,tmp)
addCriticNet(z, "tc2", targetCritic2,tmp)
# alpha: store as a 1-element tensor
addT(z, "alpha.npy", [alpha].toTensor.asType(float32), tmp)
z.close()
createDir(path.parentDir)
moveFile(tmpZip, path)
finally:
removeDir(tmp)
if fileExists(tmpZip): removeFile(tmpZip)
proc saveCheckpoint*(path: string;
actor: ActorNet;
critic1, critic2: CriticNet;
targetCritic1, targetCritic2: CriticNet;
alpha: float32;
adam: SACAdamStates) =
## Save networks + Adam states to `path` (.zip). Atomic.
let tmp = tmpDir()
createDir(tmp)
let tmpZip = path & ".tmp"
try:
var z: ZipArchive
if not z.open(tmpZip, fmWrite):
raise newException(IOError, "cannot create zip: " & tmpZip)
addActorNet(z, "actor", actor, tmp)
addCriticNet(z, "c1", critic1, tmp)
addCriticNet(z, "c2", critic2, tmp)
addCriticNet(z, "tc1", targetCritic1,tmp)
addCriticNet(z, "tc2", targetCritic2,tmp)
addT(z, "alpha.npy", [alpha].toTensor.asType(float32), tmp)
if adam.initialized:
addActorAdam(z, "adam_actor", adam.actor, tmp)
addCriticAdam(z, "adam_c1", adam.critic1, tmp)
addCriticAdam(z, "adam_c2", adam.critic2, tmp)
addAdamVar(z, "adam_alpha", adam.alpha, tmp)
# t counters (all in lockstep; store as text)
writeFile(tmp / "adam_t.txt",
$adam.actor.fc1.w.t & "\n" &
$adam.critic1.fc1.w.t & "\n" &
$adam.critic2.fc1.w.t & "\n" &
$adam.alpha.t)
z.addFile("adam_t.txt", tmp / "adam_t.txt")
z.close()
createDir(path.parentDir)
moveFile(tmpZip, path)
finally:
removeDir(tmp)
if fileExists(tmpZip): removeFile(tmpZip)
# ── Load helpers ──────────────────────────────────────────────────────────────
template loadT(name: string; tmp: string): Tensor[float32] =
read_npy[float32](tmp / name)
proc loadLinear(z: var ZipArchive; prefix, tmp: string): Linear =
z.extractFile(prefix & "_w.npy", tmp / (prefix & "_w.npy"))
z.extractFile(prefix & "_b.npy", tmp / (prefix & "_b.npy"))
result.w = read_npy[float32](tmp / (prefix & "_w.npy"))
result.b = read_npy[float32](tmp / (prefix & "_b.npy"))
proc loadLSTMCell(z: var ZipArchive; prefix, tmp: string): LSTMCell =
z.extractFile(prefix & "_wc.npy", tmp / (prefix & "_wc.npy"))
z.extractFile(prefix & "_bc.npy", tmp / (prefix & "_bc.npy"))
result.wCombined = read_npy[float32](tmp / (prefix & "_wc.npy"))
result.bCombined = read_npy[float32](tmp / (prefix & "_bc.npy"))
result.hiddenDim = result.bCombined.shape[0] div 4
proc loadActorNet(z: var ZipArchive; prefix, tmp: string): ActorNet =
result.fc1 = loadLinear(z, prefix & "_fc1", tmp)
result.lstm = loadLSTMCell(z, prefix & "_lstm", tmp)
result.fc2 = loadLinear(z, prefix & "_fc2", tmp)
result.muHead = loadLinear(z, prefix & "_mu", tmp)
result.logStdHead = loadLinear(z, prefix & "_logstd", tmp)
result.hiddenDim = result.lstm.hiddenDim
proc loadCriticNet(z: var ZipArchive; prefix, tmp: string): CriticNet =
result.fc1 = loadLinear(z, prefix & "_fc1", tmp)
result.lstm = loadLSTMCell(z, prefix & "_lstm", tmp)
result.fc2 = loadLinear(z, prefix & "_fc2", tmp)
result.fc3 = loadLinear(z, prefix & "_fc3", tmp)
result.hiddenDim = result.lstm.hiddenDim
proc loadAdamVarFromZip(z: var ZipArchive; prefix, tmp: string): AdamVar =
z.extractFile(prefix & "_m.npy", tmp / (prefix & "_m.npy"))
z.extractFile(prefix & "_v.npy", tmp / (prefix & "_v.npy"))
result.m = read_npy[float32](tmp / (prefix & "_m.npy"))
result.v = read_npy[float32](tmp / (prefix & "_v.npy"))
proc loadLinearAdam(z: var ZipArchive; prefix, tmp: string): LinearAdam =
result.w = loadAdamVarFromZip(z, prefix & "_w", tmp)
result.b = loadAdamVarFromZip(z, prefix & "_b", tmp)
proc loadLSTMCellAdam(z: var ZipArchive; prefix, tmp: string): LSTMCellAdam =
result.wCombined = loadAdamVarFromZip(z, prefix & "_wc", tmp)
result.bCombined = loadAdamVarFromZip(z, prefix & "_bc", tmp)
proc loadActorAdam(z: var ZipArchive; prefix, tmp: string): ActorAdam =
result.fc1 = loadLinearAdam(z, prefix & "_fc1", tmp)
result.lstm = loadLSTMCellAdam(z, prefix & "_lstm", tmp)
result.fc2 = loadLinearAdam(z, prefix & "_fc2", tmp)
result.muHead = loadLinearAdam(z, prefix & "_mu", tmp)
result.logStdHead = loadLinearAdam(z, prefix & "_logstd", tmp)
proc loadCriticAdam(z: var ZipArchive; prefix, tmp: string): CriticAdam =
result.fc1 = loadLinearAdam(z, prefix & "_fc1", tmp)
result.lstm = loadLSTMCellAdam(z, prefix & "_lstm", tmp)
result.fc2 = loadLinearAdam(z, prefix & "_fc2", tmp)
result.fc3 = loadLinearAdam(z, prefix & "_fc3", tmp)
type
WeightCheckpoint* = object
actor*: ActorNet
critic1*: CriticNet
critic2*: CriticNet
targetCritic1*: CriticNet
targetCritic2*: CriticNet
alpha*: float32
adam*: SACAdamStates ## initialized=false if not present in zip
proc loadCheckpoint*(path: string): WeightCheckpoint =
## Load all tensors from `path` (.zip). Raises IOError if file not found.
## Adam states loaded only if present; result.adam.initialized reflects this.
if not fileExists(path):
raise newException(IOError, "checkpoint not found: " & path)
let tmp = tmpDir()
createDir(tmp)
try:
var z: ZipArchive
if not z.open(path, fmRead):
raise newException(IOError, "cannot open zip: " & path)
result.actor = loadActorNet(z, "actor", tmp)
result.critic1 = loadCriticNet(z, "c1", tmp)
result.critic2 = loadCriticNet(z, "c2", tmp)
result.targetCritic1 = loadCriticNet(z, "tc1", tmp)
result.targetCritic2 = loadCriticNet(z, "tc2", tmp)
z.extractFile("alpha.npy", tmp / "alpha.npy")
let alphaTensor = read_npy[float32](tmp / "alpha.npy")
result.alpha = alphaTensor[0]
# Adam states — optional
var hasAdam = false
for f in z.walkFiles:
if f.startsWith("adam_"):
hasAdam = true
break
if hasAdam:
result.adam.actor = loadActorAdam(z, "adam_actor", tmp)
result.adam.critic1 = loadCriticAdam(z, "adam_c1", tmp)
result.adam.critic2 = loadCriticAdam(z, "adam_c2", tmp)
result.adam.alpha = loadAdamVarFromZip(z, "adam_alpha", tmp)
# t counters
z.extractFile("adam_t.txt", tmp / "adam_t.txt")
let ts = readFile(tmp / "adam_t.txt").strip().splitLines()
if ts.len >= 4:
let tActor = parseInt(ts[0])
let tCritic1 = parseInt(ts[1])
let tCritic2 = parseInt(ts[2])
let tAlpha = parseInt(ts[3])
# propagate t to all Adam vars
template setT(v: var AdamVar; tval: int) = v.t = tval
setT(result.adam.actor.fc1.w, tActor)
setT(result.adam.actor.fc1.b, tActor)
setT(result.adam.actor.lstm.wCombined,tActor)
setT(result.adam.actor.lstm.bCombined,tActor)
setT(result.adam.actor.fc2.w, tActor)
setT(result.adam.actor.fc2.b, tActor)
setT(result.adam.actor.muHead.w, tActor)
setT(result.adam.actor.muHead.b, tActor)
setT(result.adam.actor.logStdHead.w, tActor)
setT(result.adam.actor.logStdHead.b, tActor)
setT(result.adam.critic1.fc1.w, tCritic1)
setT(result.adam.critic1.fc1.b, tCritic1)
setT(result.adam.critic1.lstm.wCombined,tCritic1)
setT(result.adam.critic1.lstm.bCombined,tCritic1)
setT(result.adam.critic1.fc2.w, tCritic1)
setT(result.adam.critic1.fc2.b, tCritic1)
setT(result.adam.critic1.fc3.w, tCritic1)
setT(result.adam.critic1.fc3.b, tCritic1)
setT(result.adam.critic2.fc1.w, tCritic2)
setT(result.adam.critic2.fc1.b, tCritic2)
setT(result.adam.critic2.lstm.wCombined,tCritic2)
setT(result.adam.critic2.lstm.bCombined,tCritic2)
setT(result.adam.critic2.fc2.w, tCritic2)
setT(result.adam.critic2.fc2.b, tCritic2)
setT(result.adam.critic2.fc3.w, tCritic2)
setT(result.adam.critic2.fc3.b, tCritic2)
setT(result.adam.alpha, tAlpha)
result.adam.initialized = true
z.close()
finally:
removeDir(tmp)
SAC_LSTM_Bot_garage/tests/config.nims
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switch("path", "../src")
switch("path", "../../libs")
SAC_LSTM_Bot_garage/tests/test_actions.nim
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import unittest
import arraymancer
import std/math
import SAC_LSTM_Bot/actions
proc makeOutput(a0, a1, a2, a3: float): Tensor[float32] =
result = newTensor[float32](4)
result[0] = a0.float32
result[1] = a1.float32
result[2] = a2.float32
result[3] = a3.float32
suite "mapActions":
test "ACTION_DIM is 4":
check ACTION_DIM == 4
# Speed-aware turn rate
test "turn: output +1 at speed 0 -> +10 degrees":
let m = mapActions(makeOutput(1.0, 0.0, 0.0, -1.0), 0.0, 0.0)
check abs(m.turnRate - 10.0) < 1e-6
test "turn: output -1 at speed 0 -> -10 degrees":
let m = mapActions(makeOutput(-1.0, 0.0, 0.0, -1.0), 0.0, 0.0)
check abs(m.turnRate - (-10.0)) < 1e-6
test "turn: output +1 at speed 8 -> +4 degrees":
let m = mapActions(makeOutput(1.0, 0.0, 0.0, -1.0), 8.0, 0.0)
check abs(m.turnRate - 4.0) < 1e-6
test "turn: output -1 at speed 8 -> -4 degrees":
let m = mapActions(makeOutput(-1.0, 0.0, 0.0, -1.0), 8.0, 0.0)
check abs(m.turnRate - (-4.0)) < 1e-6
test "turn: output 0 -> 0 regardless of speed":
let m = mapActions(makeOutput(0.0, 0.0, 0.0, -1.0), 5.0, 0.0)
check abs(m.turnRate) < 1e-6
# Acceleration range
test "accel: output -1 -> -2.0":
let m = mapActions(makeOutput(0.0, -1.0, 0.0, -1.0), 0.0, 0.0)
check abs(m.acceleration - (-2.0)) < 1e-6
test "accel: output +1 -> +1.0":
let m = mapActions(makeOutput(0.0, 1.0, 0.0, -1.0), 0.0, 0.0)
check abs(m.acceleration - 1.0) < 1e-6
test "accel: output 0 -> midpoint -0.5":
# value*1.5 - 0.5 at value=0 -> -0.5 (correct midpoint between -2 and +1)
let m = mapActions(makeOutput(0.0, 0.0, 0.0, -1.0), 0.0, 0.0)
check abs(m.acceleration - (-0.5)) < 1e-6
# Gun turn rate
test "gun turn: output +1 -> +20 degrees":
let m = mapActions(makeOutput(0.0, 0.0, 1.0, -1.0), 0.0, 0.0)
check abs(m.gunTurnRate - 20.0) < 1e-6
test "gun turn: output -1 -> -20 degrees":
let m = mapActions(makeOutput(0.0, 0.0, -1.0, -1.0), 0.0, 0.0)
check abs(m.gunTurnRate - (-20.0)) < 1e-6
test "gun turn: output 0 -> 0":
let m = mapActions(makeOutput(0.0, 0.0, 0.0, -1.0), 0.0, 0.0)
check abs(m.gunTurnRate) < 1e-6
# Fire threshold
test "fire: output -0.5 -> no fire (firePower == 0)":
let m = mapActions(makeOutput(0.0, 0.0, 0.0, -0.5), 0.0, 0.0)
check m.firePower == 0.0
test "fire: output 0.0 -> no fire (boundary, not positive)":
let m = mapActions(makeOutput(0.0, 0.0, 0.0, 0.0), 0.0, 0.0)
check m.firePower == 0.0
test "fire: output +0.5 -> fire with correct power":
let m = mapActions(makeOutput(0.0, 0.0, 0.0, 0.5), 0.0, 0.0)
# 0.5 * 2.9 + 0.1 = 1.55
check abs(m.firePower - 1.55) < 1e-5
test "fire: output +1.0 -> fire power near 3.0":
let m = mapActions(makeOutput(0.0, 0.0, 0.0, 1.0), 0.0, 0.0)
check abs(m.firePower - 3.0) < 1e-5
test "fire: output +1.0 but gunHeat > 0 -> no fire":
let m = mapActions(makeOutput(0.0, 0.0, 0.0, 1.0), 0.0, 1.5)
check m.firePower == 0.0
test "fire: output +0.001 (just above 0) -> fires with power near 0.1":
let m = mapActions(makeOutput(0.0, 0.0, 0.0, 0.001), 0.0, 0.0)
check m.firePower > 0.0
check m.firePower < 0.2
# Speed-aware turn with negative speed (reverse)
test "turn: speed -8 (reversing) -> same magnitude as speed +8":
let fwd = mapActions(makeOutput(1.0, 0.0, 0.0, -1.0), 8.0, 0.0)
let rev = mapActions(makeOutput(1.0, 0.0, 0.0, -1.0), -8.0, 0.0)
check abs(fwd.turnRate - rev.turnRate) < 1e-6
SAC_LSTM_Bot_garage/tests/test_integration.nim
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## Tests for integration.nim (#48) — assert-based, no framework.
## Covers: TrainingMsg channel round-trip (plain arrays through a channel),
## drain-then-train NewBattle/Shutdown handling, trainPass safety below canSample.
import arraymancer except Linear
import std/[locks, json, os]
import tankroyale_botapi # updateBotNames: seed the vendored id->name table
import SAC_LSTM_Bot/integration
import SAC_LSTM_Bot/state # STATE_DIM
import SAC_LSTM_Bot/actions # ACTION_DIM
import SAC_LSTM_Bot/training # initSACTrainer
import SAC_LSTM_Bot/replay_buffer
# ── 1. TrainingMsg round-trips through a channel with arrays intact ──────────
block:
var ch: Channel[TrainingMsg]
ch.open(4)
var msg = TrainingMsg(kind: tmkTransition)
for i in 0 ..< STATE_DIM:
msg.state[i] = float32(i) * 0.5'f32
msg.nextState[i] = float32(i) * 2.0'f32
for i in 0 ..< ACTION_DIM:
msg.action[i] = float32(i) - 2.0'f32
msg.reward = -1.25'f32
msg.done = true
assert ch.trySend(msg)
assert ch.trySend(TrainingMsg(kind: tmkNewBattle, enemyId: 4242))
assert ch.trySend(TrainingMsg(kind: tmkShutdown))
let r1 = ch.recv()
assert r1.kind == tmkTransition, "first msg is a transition"
for i in 0 ..< STATE_DIM:
assert r1.state[i] == float32(i) * 0.5'f32, "state round-trip at " & $i
assert r1.nextState[i] == float32(i) * 2.0'f32, "nextState round-trip at " & $i
for i in 0 ..< ACTION_DIM:
assert r1.action[i] == float32(i) - 2.0'f32, "action round-trip at " & $i
assert r1.reward == -1.25'f32 and r1.done
let r2 = ch.recv()
assert r2.kind == tmkNewBattle and r2.enemyId == 4242
let r3 = ch.recv()
assert r3.kind == tmkShutdown
ch.close()
# closed + empty -> tryRecv reports no data (Nim 2.2: recv would block forever)
let (ok4, _) = ch.tryRecv()
assert not ok4, "closed channel must report dataAvailable=false"
echo "PASS TrainingMsg channel round-trip"
# ── 2. NewBattle clears only when the opponent NAME changes; Shutdown stops ───
block:
# Seed the API's id->name table (v1.0.1) for name-based identity (#49).
updateBotNames(parseJson(
"""{"bots":[{"id":7,"name":"Corners"},{"id":8,"name":"Crazy"}]}"""))
assert opponentKey(7) == "Corners", "known id resolves to name"
assert opponentKey(99) == "99", "unknown id falls back to numeric string"
var st: TrainState
st.trainer = initSACTrainer(STATE_DIM, ACTION_DIM)
st.buf = newReplayBuffer(64, STATE_DIM, ACTION_DIM, burnIn = 2, trainWindow = 3)
assert handleTrainingMsg(st, TrainingMsg(kind: tmkNewBattle, enemyId: 7))
assert st.lastEnemyKey == "Corners"
for i in 0 ..< 5:
var m = TrainingMsg(kind: tmkTransition)
m.reward = float32(i)
assert handleTrainingMsg(st, m)
assert st.buf.len == 5, "transitions stored"
# Same opponent -> buffer kept (Q12a).
assert handleTrainingMsg(st, TrainingMsg(kind: tmkNewBattle, enemyId: 7))
assert st.buf.len == 5, "same opponent must NOT clear"
# Opponent changed -> clear.
assert handleTrainingMsg(st, TrainingMsg(kind: tmkNewBattle, enemyId: 8))
assert st.buf.len == 0, "opponent change must clear"
assert st.lastEnemyKey == "Crazy"
# Nameless window (pre-BotListUpdate) is a distinct key.
assert handleTrainingMsg(st, TrainingMsg(kind: tmkNewBattle, enemyId: 99))
assert st.lastEnemyKey == "99" and st.buf.len == 0
# Shutdown stops the caller's loop.
assert not handleTrainingMsg(st, TrainingMsg(kind: tmkShutdown))
echo "PASS name-keyed NewBattle clear / Shutdown"
# ── 2b. bumpRoundCounter increments per call, cold-starts at 1 ────────────────
block:
let tmp = getTempDir() / "sac_test_weights_" & $getCurrentProcessId()
putEnv("SACLSTM_WEIGHTS_PATH", tmp / "sac_latest.zip")
bumpRoundCounter()
bumpRoundCounter()
assert readFile(tmp / "round_counter.txt") == "2", "counter increments per round"
echo "PASS bumpRoundCounter"
# ── 3. trainPass is a safe no-op below canSample (no steps, no publish) ───────
block:
var st: TrainState
st.trainer = initSACTrainer(STATE_DIM, ACTION_DIM)
st.buf = newReplayBuffer(64, STATE_DIM, ACTION_DIM, burnIn = 2, trainWindow = 3)
for i in 0 ..< 4:
assert handleTrainingMsg(st, TrainingMsg(kind: tmkTransition))
trainPass(st, 4) # 4 < burnIn+trainWindow = 5
assert st.stepCount == 0, "no gradient steps below canSample"
echo "PASS trainPass no-op below canSample"
# ── 4. Flat snapshot layout: pack/unpack round-trips weights exactly ──────────
block:
let t0 = initSACTrainer(STATE_DIM, ACTION_DIM)
let fs = packFull(t0)
assert fs.data.len == actorSize(t0.actor.hiddenDim) + 4 * criticSize(t0.actor.hiddenDim) + 1
let (a, c1, c2, tc1, tc2, alpha) = unpackFull(fs)
assert a.hiddenDim == t0.actor.hiddenDim
assert c1.fc3.b.shape[0] == 1
let fw = a.muHead.w.flatten()
let fw0 = t0.actor.muHead.w.flatten()
for i in 0 ..< fw.size:
assert fw[i] == fw0[i], "actor mu weights round-trip"
for i in 0 ..< c2.lstm.bCombined.size:
assert c2.lstm.bCombined[i] == t0.critic2.lstm.bCombined[i], "critic lstm bias round-trip"
assert alpha == t0.alpha()
discard tc1
echo "PASS flat snapshot pack/unpack round-trip"
# ── 5. Lever 3 (#59): metricsLine emits exactly the exposed trainer scalars ───
block:
let line = metricsLine(1787394115.123, 42, 500, 20, 20,
SACMetrics(criticLoss: 0.5'f32, actorLoss: -1.5'f32,
alphaLoss: 0.25'f32, alpha: 2.0'f32))
let j = parseJson(line) # throws on malformed JSONL
assert j["steps"].getInt() == 42 and j["buffer_size"].getInt() == 500
assert j["drained"].getInt() == 20 and j["grad_steps"].getInt() == 20
assert abs(j["critic_loss"].getFloat() - 0.5) < 1e-3
assert abs(j["actor_loss"].getFloat() + 1.5) < 1e-3
assert abs(j["alpha_loss"].getFloat() - 0.25) < 1e-3
assert abs(j["alpha"].getFloat() - 2.0) < 1e-3
assert j["epoch"].getFloat() > 1e9
echo "PASS metricsLine JSONL scalars"
# ── 6. Lever 4 (#59): SACLSTM_EVAL_MODE=1 suppresses training input ───────────
block:
putEnv("SACLSTM_EVAL_MODE", "1")
# Gate fires before any channel traffic: false = dropped, nothing enqueued.
assert not sendTrainingMsg(TrainingMsg(kind: tmkTransition)),
"eval mode must drop transitions"
assert not sendTrainingMsg(TrainingMsg(kind: tmkNewBattle, enemyId: 7)),
"eval mode must drop NewBattle (no buffer clears from eval)"
delEnv("SACLSTM_EVAL_MODE")
echo "PASS eval-mode training-input suppression"
SAC_LSTM_Bot_garage/tests/test_network.nim
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import unittest
import arraymancer
import std/[math, os]
import SAC_LSTM_Bot/network
const
STATE_DIM = 20
ACTION_DIM = 4
suite "ActorNet":
setup:
let actor = initActorNet(STATE_DIM)
let state = randomNormalTensor[float32](STATE_DIM)
let ls0 = zeroState(actor.hiddenDim)
test "forward output shapes":
let (actions, lp, ls1) = actorForward(actor, state, ls0)
check actions.shape == [4]
check ls1.h.shape == [actor.hiddenDim]
check ls1.c.shape == [actor.hiddenDim]
check not lp.isNaN
test "actions clamped in (-1, 1) — tanh output":
let (actions, _, _) = actorForward(actor, state, ls0)
for i in 0 ..< 4:
check actions[i] > -1.0'f32
check actions[i] < 1.0'f32
test "hidden state propagates (h/c change after step)":
let (_, _, ls1) = actorForward(actor, state, ls0)
# h' should differ from zero init for non-trivial input
var hChanged = false
for i in 0 ..< actor.hiddenDim:
if abs(ls1.h[i] - ls0.h[i]) > 1e-7'f32:
hChanged = true
break
check hChanged
test "deterministic mode: same input → same output":
let (a1, _, _) = actorForward(actor, state, ls0, deterministic = true)
let (a2, _, _) = actorForward(actor, state, ls0, deterministic = true)
for i in 0 ..< 4:
check abs(a1[i] - a2[i]) < 1e-7'f32
test "stochastic mode: outputs may differ (sampling)":
# Run many times; at least one pair should differ
let (a1, _, _) = actorForward(actor, state, ls0)
let (a2, _, _) = actorForward(actor, state, ls0)
var anyDiff = false
for i in 0 ..< 4:
if abs(a1[i] - a2[i]) > 1e-7'f32:
anyDiff = true
break
check anyDiff
suite "CriticNet":
setup:
let critic = initCriticNet(STATE_DIM, ACTION_DIM)
let state = randomNormalTensor[float32](STATE_DIM)
let actions = randomNormalTensor[float32](ACTION_DIM)
let stateAct = concat(state, actions, axis = 0)
let ls0 = zeroState(critic.hiddenDim)
test "forward output shape":
let (q, ls1) = criticForward(critic, stateAct, ls0)
check ls1.h.shape == [critic.hiddenDim]
check ls1.c.shape == [critic.hiddenDim]
# q is scalar float — just ensure it doesn't NaN
check not q.isNaN
test "hidden state propagates":
let (_, ls1) = criticForward(critic, stateAct, ls0)
var hChanged = false
for i in 0 ..< critic.hiddenDim:
if abs(ls1.h[i] - ls0.h[i]) > 1e-7'f32:
hChanged = true
break
check hChanged
suite "Dual critics":
test "two independent critics produce different Q values":
let c1 = initCriticNet(STATE_DIM, ACTION_DIM)
let c2 = initCriticNet(STATE_DIM, ACTION_DIM)
let sa = randomNormalTensor[float32](STATE_DIM + ACTION_DIM)
let ls = zeroState(c1.hiddenDim)
let (q1, _) = criticForward(c1, sa, ls)
let (q2, _) = criticForward(c2, sa, ls)
check abs(q1 - q2) > 1e-7'f32
suite "Hidden size config":
test "hidden size 128 works":
putEnv("SACLSTM_HIDDEN_SIZE", "128")
let actor = initActorNet(STATE_DIM)
let state = randomNormalTensor[float32](STATE_DIM)
let ls0 = zeroState(128)
let (actions, _, ls1) = actorForward(actor, state, ls0)
check actions.shape == [4]
check ls1.h.shape == [128]
putEnv("SACLSTM_HIDDEN_SIZE", "256")
SAC_LSTM_Bot_garage/tests/test_replay_buffer.nim
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## Tests for replay_buffer.nim — assert-based, no framework.
import arraymancer
import std/sequtils
import SAC_LSTM_Bot/replay_buffer
import SAC_LSTM_Bot/state # STATE_DIM
const
S_DIM = STATE_DIM # 35
A_DIM = 4
proc makeTrans(reward: float32; done: bool): Transition =
Transition(
state: zeros[float32](S_DIM),
action: zeros[float32](A_DIM),
reward: reward,
nextState: zeros[float32](S_DIM),
done: done
)
# ── 1. len and canSample on empty buffer ──────────────────────────────────────
block:
var buf = newReplayBuffer(100, S_DIM, A_DIM, burnIn = 8, trainWindow = 16)
assert buf.len == 0, "empty len"
assert not buf.canSample, "empty canSample"
echo "PASS empty buffer"
# ── 2. len grows, canSample becomes true ──────────────────────────────────────
block:
var buf = newReplayBuffer(100, S_DIM, A_DIM, burnIn = 2, trainWindow = 3)
for i in 0 ..< 4:
buf.add(makeTrans(float32(i), false))
assert buf.len == 4
assert not buf.canSample, "needs 5 to sample"
buf.add(makeTrans(99, false))
assert buf.len == 5
assert buf.canSample, "5 transitions, seqLen=5 → canSample"
echo "PASS len + canSample"
# ── 3. Ring buffer wraps at capacity ─────────────────────────────────────────
block:
var buf = newReplayBuffer(10, S_DIM, A_DIM, burnIn = 2, trainWindow = 3)
for i in 0 ..< 15:
buf.add(makeTrans(float32(i), false))
assert buf.len == 10, "wraps at capacity, len stays 10"
echo "PASS wrap at capacity"
# ── 4. Sampled sequences have correct length ──────────────────────────────────
block:
var buf = newReplayBuffer(200, S_DIM, A_DIM, burnIn = 4, trainWindow = 6)
for i in 0 ..< 50:
buf.add(makeTrans(float32(i), false))
let seqs = buf.sampleSequences(8)
assert seqs.len > 0, "should have valid starts"
for s in seqs:
assert s.burnIn.len == 4, "burnIn len"
assert s.train.len == 6, "train len"
echo "PASS sequence length"
# ── 5. Burn-in / train split is correct ───────────────────────────────────────
block:
# Fill with distinct rewards so we can identify positions
var buf = newReplayBuffer(200, S_DIM, A_DIM, burnIn = 3, trainWindow = 4)
for i in 0 ..< 30:
buf.add(makeTrans(float32(i), false))
let seqs = buf.sampleSequences(1)
assert seqs.len == 1
let s = seqs[0]
# The 4th reward of the sequence (index 3) must match s.train[0].reward
# and s.burnIn[2].reward must be s.burnIn[2].reward (just check no overlap)
let allRewards = s.burnIn.mapIt(it.reward) & s.train.mapIt(it.reward)
# consecutive integer rewards → each must be strictly increasing by 1
var ok = true
for i in 1 ..< allRewards.len:
if allRewards[i] != allRewards[i-1] + 1.0f32:
ok = false
break
assert ok, "burn-in and train must form a contiguous sequence"
echo "PASS burn-in/train split"
# ── 6. Sequences never cross a done=true boundary ─────────────────────────────
block:
var buf = newReplayBuffer(200, S_DIM, A_DIM, burnIn = 2, trainWindow = 3)
# Episode 1: transitions 0..4 (done at index 4)
for i in 0 ..< 4:
buf.add(makeTrans(float32(i), false))
buf.add(makeTrans(99, true)) # battle end at index 4
# Episode 2: transitions 5..14
for i in 5 ..< 15:
buf.add(makeTrans(float32(i), false))
let seqs = buf.sampleSequences(20)
for s in seqs:
# No transition in burnIn (except the last) or train (except the last)
# may have done=true, since that would mean the next step crosses a boundary.
for i in 0 ..< s.burnIn.len - 1:
assert not s.burnIn[i].done, "done in middle of burnIn"
for i in 0 ..< s.train.len - 1:
assert not s.train[i].done, "done in middle of train"
# The join between burnIn and train must not cross a done=true
if s.burnIn.len > 0:
assert not s.burnIn[^1].done, "done at end of burnIn crosses boundary to train"
echo "PASS no cross-boundary sequences"
# ── 7. canSample false when buffer too small ───────────────────────────────────
block:
var buf = newReplayBuffer(100, S_DIM, A_DIM, burnIn = 8, trainWindow = 16)
for i in 0 ..< 23: # seqLen = 24; 23 < 24
buf.add(makeTrans(float32(i), false))
assert not buf.canSample, "23 < 24 seqLen"
buf.add(makeTrans(23, false))
assert buf.canSample, "24 == seqLen"
echo "PASS canSample threshold"
# ── 8. Empty buffer doesn't crash on sample ────────────────────────────────────
block:
var buf = newReplayBuffer(100, S_DIM, A_DIM, burnIn = 8, trainWindow = 16)
let seqs = buf.sampleSequences(4)
assert seqs.len == 0, "empty buffer → empty result"
echo "PASS empty sample"
# ── 9. Single episode (no done except at very end) ────────────────────────────
block:
var buf = newReplayBuffer(200, S_DIM, A_DIM, burnIn = 3, trainWindow = 5)
for i in 0 ..< 19:
buf.add(makeTrans(float32(i), false))
buf.add(makeTrans(19, true)) # last
let seqs = buf.sampleSequences(5)
assert seqs.len > 0, "should find valid starts"
for s in seqs:
assert s.burnIn.len == 3
assert s.train.len == 5
echo "PASS single episode"
# ── 10. Multiple short episodes: all boundaries respected ─────────────────────
block:
var buf = newReplayBuffer(300, S_DIM, A_DIM, burnIn = 2, trainWindow = 3)
# 10 episodes of 5 transitions each (done at end of each episode)
var reward = 0'f32
for ep in 0 ..< 10:
for i in 0 ..< 4:
buf.add(makeTrans(reward, false))
reward += 1
buf.add(makeTrans(reward, true)) # battle end
reward += 1
let seqs = buf.sampleSequences(30)
assert seqs.len > 0
for s in seqs:
# No done in the middle of any sequence
let all = s.burnIn & s.train
for i in 0 ..< all.len - 1:
assert not all[i].done, "boundary crossed in multi-episode test"
echo "PASS multiple episodes"
echo "ALL TESTS PASSED"
SAC_LSTM_Bot_garage/tests/test_rewards.nim
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## Assert-based tests for rewards.nim.
## Run: nim c -r tests/test_rewards.nim
import std/[math, strformat]
import SAC_LSTM_Bot/rewards
template check(cond: bool, msg: string) =
if not cond:
quit("FAIL: " & msg, 1)
# ── computeReward ─────────────────────────────────────────────────────────────
block damageInflicted:
# p=1: 1.25 * (6*1 - 2) = 5 (lever 2 aggression mult, #59)
check abs(computeReward(damageInflicted = 1.0) - 5.0) < 1e-9, "p=1 damage = +5"
# p=3: 1.25 * (6*3 - 2) = 20
check abs(computeReward(damageInflicted = 3.0) - 20.0) < 1e-9, "p=3 damage = +20"
# low-power spam stays unprofitable: 1.25*(6*0.1-2) < 0
check computeReward(damageInflicted = 0.1) < 0.0, "p=0.1 spam still negative"
block damageReceived:
# p_e=1: -(6*1 - 2) = -4 (unchanged by lever 2)
check abs(computeReward(damageReceived = 1.0) - (-4.0)) < 1e-9, "p_e=1 received = -4"
# p_e=3: -(6*3 - 2) = -16
check abs(computeReward(damageReceived = 3.0) - (-16.0)) < 1e-9, "p_e=3 received = -16"
block hitBonus:
# flat +0.5 per landed shot: p=1 hit -> 5.0 + 0.5
check abs(computeReward(damageInflicted = 1.0, hitCount = 1) - 5.5) < 1e-9,
"p=1 hit = +5.5"
# two hits in one step: 1.25*(6*2-2) + 2*0.5 = 12.5 + 1.0 = 13.5
check abs(computeReward(damageInflicted = 2.0, hitCount = 2) - 13.5) < 1e-9,
"two hits = +13.5"
block ramTaken:
# flat per victim collision (#59)
check abs(computeReward(ramTakenCount = 1) - (-3.0)) < 1e-9, "ram taken x1 = -3"
check abs(computeReward(ramTakenCount = 2) - (-6.0)) < 1e-9, "ram taken x2 = -6"
block chargeDeterrent:
# zero-damage case at half threshold depth: -2 * (1 - 0.06/0.12) = -1
let rHalf = computeReward(enemyDistFrac = 0.06)
check abs(rHalf - (-1.0)) < 1e-9, "charge at frac 0.06 = -1"
# at zero distance: full ceiling
check abs(computeReward(enemyDistFrac = 0.0) - (-2.0)) < 1e-9, "charge at frac 0 = -2"
# at/beyond threshold and no-contact sentinel: no penalty
check abs(computeReward(enemyDistFrac = 0.12)) < 1e-9, "at threshold = 0"
check abs(computeReward(enemyDistFrac = 0.5)) < 1e-9, "beyond threshold = 0"
check abs(computeReward(enemyDistFrac = 2.0)) < 1e-9, "no-contact sentinel = 0"
# suppressed while dealing damage that step (fighting back at close range is fine)
let rFight = computeReward(damageInflicted = 1.0, enemyDistFrac = 0.06)
check abs(rFight - 5.0) < 1e-9, "dealing damage cancels charge penalty"
block wallHit:
check abs(computeReward(wallHitTicks = 1) - (-5.0)) < 1e-9, "1 wall tick = -5"
block wastedShot:
# p=2: -0.1 * 2 = -0.2
check abs(computeReward(wastedShotPower = 2.0) - (-0.2)) < 1e-9, "missed p=2 = -0.2"
block winLoss:
# terminal terms stay dominant over shaping (#59 scale discipline)
check abs(computeReward(win = true) - 20.0) < 1e-9, "win = +20"
check abs(computeReward(loss = true) - (-10.0)) < 1e-9, "loss = -10"
# ── RewardNormalizer cold start ───────────────────────────────────────────────
block coldStart:
var rn: RewardNormalizer
# 0 samples
let v0 = rn.normalize(99.0)
check not isNaN(v0), "0 samples: not NaN"
check classify(v0) != fcInf and classify(v0) != fcNegInf, "0 samples: not inf"
check abs(v0) < 1e-9, "0 samples: returns 0"
# 1 sample (variance undefined)
rn.update(5.0)
let v1 = rn.normalize(5.0)
check not isNaN(v1), "1 sample: not NaN"
check classify(v1) != fcInf and classify(v1) != fcNegInf, "1 sample: not inf"
check abs(v1) < 1e-9, "1 sample: returns 0"
# ── Running normalization convergence ─────────────────────────────────────────
block convergence:
var rn: RewardNormalizer
# Feed 1000 identical samples of 5.0 — mean=5.0, std=0 → normalizer returns ~0
for _ in 0 ..< 1000:
rn.update(5.0)
let v = rn.normalize(5.0)
check not isNaN(v), "convergence: not NaN"
check classify(v) != fcInf and classify(v) != fcNegInf, "convergence: not inf"
# (5 - 5) / (0 + eps) = 0
check abs(v) < 1e-6, "convergence to mean: normalized ≈ 0"
block knownMeanStd:
# Insert samples -1 and +1 repeatedly → mean=0, std=1
var rn: RewardNormalizer
for _ in 0 ..< 500:
rn.update(-1.0)
rn.update( 1.0)
# normalize(1.0) ≈ (1 - 0) / (1 + eps) ≈ 1
let vPos = rn.normalize(1.0)
check abs(vPos - 1.0) < 1e-4, &"normalize(+1) ≈ +1, got {vPos}"
let vNeg = rn.normalize(-1.0)
check abs(vNeg - (-1.0)) < 1e-4, &"normalize(-1) ≈ -1, got {vNeg}"
let vMid = rn.normalize(0.0)
check abs(vMid) < 1e-4, &"normalize(0) ≈ 0, got {vMid}"
echo "test_rewards: all passed"
SAC_LSTM_Bot_garage/tests/test_state.nim
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## Tests for state.nim — assert-based, no framework.
import std/math
import arraymancer
import SAC_LSTM_Bot/state
proc makeBase(): GameState =
result.arenaWidth = 1200.0
result.arenaHeight = 800.0
result.x = 600.0; result.y = 400.0
result.direction = 90.0; result.speed = 4.0
result.energy = 50.0
result.gunDirection = 90.0; result.gunHeat = 0.5
proc allInRange(t: Tensor[float32]): bool =
for v in t:
if v < -1.01f32 or v > 1.01f32: return false
true
proc hasNaN(t: Tensor[float32]): bool =
for v in t:
if v.float64.isNaN: return true
false
# 1. Correct shape
block:
let gs = makeBase()
let t = buildState(gs)
assert t.shape[0] == STATE_DIM, "wrong dim: " & $t.shape[0]
echo "PASS shape"
# 2. All values in [-1, 1] for typical input
block:
var gs = makeBase()
gs.hasContact = true
gs.enemy = EnemyData(x: 800.0, y: 300.0, direction: 45.0, speed: 6.0,
energy: 80.0, hasFired: true, lastFirePower: 2.0,
prevSpeed: 5.0, prevDirection: 40.0, hasPrevScan: true)
gs.bulletCount = 1
gs.bullets[0] = BulletData(x: 610.0, y: 410.0, power: 1.0)
gs.ticksSinceLastScan = 10
let t = buildState(gs)
assert not hasNaN(t), "NaN in tensor"
assert allInRange(t), "value out of [-1,1]"
echo "PASS range"
# 3. Missing scan → valid tensor, no NaN, zeros for enemy fields
block:
let gs = makeBase() # hasContact = false
let t = buildState(gs)
assert not hasNaN(t), "NaN with no scan"
for i in 7 .. 17:
assert t[i] == 0.0f32, "enemy slot " & $i & " should be 0"
echo "PASS no-scan zeros"
# 4. Bullet tracking: 0, 1, 2, 3 bullets
block:
for n in 0 .. 3:
var gs = makeBase()
gs.bulletCount = n
for i in 0 ..< n:
gs.bullets[i] = BulletData(x: float64(600 + i * 10), y: float64(400 + i * 5), power: 1.0)
let t = buildState(gs)
assert not hasNaN(t), "NaN with " & $n & " bullets"
# slots beyond bulletCount must be 0
for i in n ..< 3:
let base = 22 + i * 4
for j in 0 ..< 4:
assert t[base + j] == 0.0f32, "bullet slot " & $i & " slot " & $j & " should be 0"
echo "PASS bullet tracking 0-3"
# 5. Scan staleness increments and clamps
block:
var gs = makeBase()
gs.hasContact = true
gs.ticksSinceLastScan = 0
assert buildState(gs)[34] == 0.0f32, "staleness at 0"
gs.ticksSinceLastScan = 15
let mid = buildState(gs)[34]
assert mid > 0.0f32 and mid < 1.0f32, "staleness mid not in (0,1)"
gs.ticksSinceLastScan = 30
assert buildState(gs)[34] == 1.0f32, "staleness at 30"
gs.ticksSinceLastScan = 60
assert buildState(gs)[34] == 1.0f32, "staleness clamp at 60"
echo "PASS staleness"
echo "ALL TESTS PASSED"
SAC_LSTM_Bot_garage/tests/test_training.nim
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## test_training.nim — stdlib unittest for SAC training module.
import unittest
import arraymancer
import std/[math, random, os]
import SAC_LSTM_Bot/network
import SAC_LSTM_Bot/weights
import SAC_LSTM_Bot/replay_buffer
import SAC_LSTM_Bot/training
const
STATE_DIM = 10
ACTION_DIM = 4
HIDDEN = 16 # small for speed; override via env not needed in tests
proc makeTrainer(): SACTrainer =
## Small trainer for tests — override hidden size via env before calling.
putEnv("SACLSTM_HIDDEN_SIZE", "16")
initSACTrainer(STATE_DIM, ACTION_DIM)
proc makeBuffer(): ReplayBuffer =
newReplayBuffer(capacity = 2000, stateDim = STATE_DIM, actionDim = ACTION_DIM,
burnIn = 4, trainWindow = 8)
proc randState(): Tensor[float32] =
randomNormalTensor[float32](STATE_DIM)
proc randAction(): Tensor[float32] =
randomTensor[float32](ACTION_DIM, 1.0'f32) *. 2.0'f32 -. 1.0'f32 # uniform [-1,1]
proc fillBuffer(buf: var ReplayBuffer; n: int; donePeriod = 20) =
for i in 0 ..< n:
let t = Transition(
state: randState(),
action: randAction(),
reward: rand(-1.0'f32 .. 1.0'f32),
nextState: randState(),
done: (i mod donePeriod == donePeriod - 1))
buf.add(t)
proc isFinite(x: float32): bool =
not (x != x) and x < Inf and x > -Inf # not NaN and not Inf
suite "SACTrainer — basic update":
setup:
randomize(42)
var trainer = makeTrainer()
var buf = makeBuffer()
fillBuffer(buf, 1000)
let seqs = buf.sampleSequences(4)
test "sampleSequences returns non-empty batch":
check seqs.len > 0
test "sacUpdate returns finite losses":
let m = sacUpdate(trainer, seqs)
check isFinite(m.criticLoss)
check isFinite(m.actorLoss)
check isFinite(m.alphaLoss)
test "alpha stays positive after update":
var t2 = trainer
discard sacUpdate(t2, seqs)
check t2.alpha() > 0.0'f32
test "critic1 weights change after update":
let w_before = trainer.critic1.fc3.w.clone()
discard sacUpdate(trainer, seqs)
let w_after = trainer.critic1.fc3.w
var changed = false
for i in 0 ..< w_before.size:
if abs(w_before.unsafe_raw_offset[i] - w_after.unsafe_raw_offset[i]) > 1e-10'f32:
changed = true
break
check changed
test "critic2 weights change after update":
let w_before = trainer.critic2.fc3.w.clone()
discard sacUpdate(trainer, seqs)
let w_after = trainer.critic2.fc3.w
var changed = false
for i in 0 ..< w_before.size:
if abs(w_before.unsafe_raw_offset[i] - w_after.unsafe_raw_offset[i]) > 1e-10'f32:
changed = true
break
check changed
test "actor weights change after update":
let w_before = trainer.actor.fc1.w.clone()
discard sacUpdate(trainer, seqs)
let w_after = trainer.actor.fc1.w
var changed = false
for i in 0 ..< w_before.size:
if abs(w_before.unsafe_raw_offset[i] - w_after.unsafe_raw_offset[i]) > 1e-10'f32:
changed = true
break
check changed
test "soft target update: target moves toward critic":
## After update, target fc3.w should be closer to critic1.fc3.w than before.
let targetBefore = trainer.targetCritic1.fc3.w.clone()
let criticW = trainer.critic1.fc3.w.clone()
discard sacUpdate(trainer, seqs)
let targetAfter = trainer.targetCritic1.fc3.w
# Distance before: ||targetBefore - criticW||
var distBefore = 0.0'f32
for i in 0 ..< targetBefore.size:
let d = targetBefore.unsafe_raw_offset[i] - criticW.unsafe_raw_offset[i]
distBefore += d * d
# Distance after: ||targetAfter - criticW_new|| (critic changed too, use original for ref)
var distAfter = 0.0'f32
for i in 0 ..< targetAfter.size:
let d = targetAfter.unsafe_raw_offset[i] - criticW.unsafe_raw_offset[i]
distAfter += d * d
# Target moved toward critic (distance decreased).
# With tau=0.005, it moves a tiny bit — just check direction.
check distAfter <= distBefore + 1e-3'f32 # soft bound; critic also moves
test "empty sequences is a no-op":
let m = sacUpdate(trainer, @[])
check m.criticLoss == 0.0'f32
check m.actorLoss == 0.0'f32
check m.alpha == 0.0'f32
suite "SACTrainer — done=true terminal transitions":
test "update with done=true transitions produces finite losses":
randomize(7)
putEnv("SACLSTM_HIDDEN_SIZE", "16")
var trainer = makeTrainer()
var buf = makeBuffer()
# Fill with short episodes: done every 12 steps (burnIn=4, trainWindow=8 → seqLen=12)
fillBuffer(buf, 800, donePeriod = 12)
let seqs = buf.sampleSequences(2)
if seqs.len > 0:
let m = sacUpdate(trainer, seqs)
check isFinite(m.criticLoss)
check isFinite(m.actorLoss)
check isFinite(m.alphaLoss)
check m.alpha > 0.0'f32
suite "SACTrainer — multiple updates":
test "three sequential updates all stay finite":
randomize(99)
putEnv("SACLSTM_HIDDEN_SIZE", "16")
var trainer = makeTrainer()
var buf = makeBuffer()
fillBuffer(buf, 1000)
for _ in 1..3:
let seqs = buf.sampleSequences(4)
if seqs.len > 0:
let m = sacUpdate(trainer, seqs)
check isFinite(m.criticLoss)
check isFinite(m.actorLoss)
check m.alpha > 0.0'f32
SAC_LSTM_Bot_garage/tests/test_weights.nim
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import unittest
import arraymancer
import zip/zipfiles
import std/[os, math, strutils]
import SAC_LSTM_Bot/network
import SAC_LSTM_Bot/weights
const
STATE_DIM = 20
ACTION_DIM = 4
proc tensorsEqual(a, b: Tensor[float32]; tol: float32 = 1e-6'f32): bool =
if a.shape != b.shape: return false
for i in 0 ..< a.size:
if abs(a.unsafe_raw_offset[i] - b.unsafe_raw_offset[i]) > tol: return false
true
suite "saveWeights / loadCheckpoint":
setup:
let actor = initActorNet(STATE_DIM)
let c1 = initCriticNet(STATE_DIM, ACTION_DIM)
let c2 = initCriticNet(STATE_DIM, ACTION_DIM)
let tc1 = initCriticNet(STATE_DIM, ACTION_DIM)
let tc2 = initCriticNet(STATE_DIM, ACTION_DIM)
let alpha = 0.2'f32
let zipPath = getTempDir() / "test_weights_latest.zip"
teardown:
if fileExists(zipPath): removeFile(zipPath)
test "creates a valid zip file":
saveWeights(zipPath, actor, c1, c2, tc1, tc2, alpha)
check fileExists(zipPath)
var z: ZipArchive
check z.open(zipPath, fmRead)
var count = 0
for f in z.walkFiles: inc count
z.close()
check count > 0
test "all entries are .npy files (+ alpha.npy)":
saveWeights(zipPath, actor, c1, c2, tc1, tc2, alpha)
var z: ZipArchive
discard z.open(zipPath, fmRead)
var allNpy = true
for f in z.walkFiles:
if not f.endsWith(".npy"): allNpy = false
z.close()
check allNpy
test "round-trip: actor weights preserved":
saveWeights(zipPath, actor, c1, c2, tc1, tc2, alpha)
let ck = loadCheckpoint(zipPath)
check tensorsEqual(actor.fc1.w, ck.actor.fc1.w)
check tensorsEqual(actor.fc1.b, ck.actor.fc1.b)
check tensorsEqual(actor.lstm.wCombined, ck.actor.lstm.wCombined)
check tensorsEqual(actor.lstm.bCombined, ck.actor.lstm.bCombined)
check tensorsEqual(actor.fc2.w, ck.actor.fc2.w)
check tensorsEqual(actor.muHead.w, ck.actor.muHead.w)
check tensorsEqual(actor.logStdHead.w, ck.actor.logStdHead.w)
test "round-trip: critic weights preserved":
saveWeights(zipPath, actor, c1, c2, tc1, tc2, alpha)
let ck = loadCheckpoint(zipPath)
check tensorsEqual(c1.fc1.w, ck.critic1.fc1.w)
check tensorsEqual(c1.lstm.wCombined, ck.critic1.lstm.wCombined)
check tensorsEqual(c1.fc3.w, ck.critic1.fc3.w)
check tensorsEqual(tc1.fc1.w, ck.targetCritic1.fc1.w)
check tensorsEqual(tc2.fc1.w, ck.targetCritic2.fc1.w)
test "round-trip: alpha preserved":
saveWeights(zipPath, actor, c1, c2, tc1, tc2, alpha)
let ck = loadCheckpoint(zipPath)
check abs(ck.alpha - alpha) < 1e-6'f32
test "round-trip: hiddenDim reconstructed":
saveWeights(zipPath, actor, c1, c2, tc1, tc2, alpha)
let ck = loadCheckpoint(zipPath)
check ck.actor.hiddenDim == actor.hiddenDim
check ck.critic1.hiddenDim == c1.hiddenDim
test "adam not present → initialized=false":
saveWeights(zipPath, actor, c1, c2, tc1, tc2, alpha)
let ck = loadCheckpoint(zipPath)
check not ck.adam.initialized
suite "saveCheckpoint (with Adam)":
setup:
let actor = initActorNet(STATE_DIM)
let c1 = initCriticNet(STATE_DIM, ACTION_DIM)
let c2 = initCriticNet(STATE_DIM, ACTION_DIM)
let tc1 = initCriticNet(STATE_DIM, ACTION_DIM)
let tc2 = initCriticNet(STATE_DIM, ACTION_DIM)
let alpha = 0.1'f32
var adam = initSACAdamStates(actor, c1, c2)
# put some non-zero values in Adam state
adam.actor.fc1.w.m[0, 0] = 0.5'f32
adam.actor.fc1.w.t = 42
adam.critic1.fc1.w.t = 7
adam.alpha.t = 99
let zipPath = getTempDir() / "test_checkpoint.zip"
teardown:
if fileExists(zipPath): removeFile(zipPath)
test "round-trip: Adam m tensor":
saveCheckpoint(zipPath, actor, c1, c2, tc1, tc2, alpha, adam)
let ck = loadCheckpoint(zipPath)
check ck.adam.initialized
check abs(ck.adam.actor.fc1.w.m[0, 0] - 0.5'f32) < 1e-6'f32
test "round-trip: Adam t counters":
saveCheckpoint(zipPath, actor, c1, c2, tc1, tc2, alpha, adam)
let ck = loadCheckpoint(zipPath)
check ck.adam.actor.fc1.w.t == 42
check ck.adam.critic1.fc1.w.t == 7
check ck.adam.alpha.t == 99
suite "Atomic save":
test "temp file is cleaned up after successful save":
let zipPath = getTempDir() / "test_atomic.zip"
let tmpZip = zipPath & ".tmp"
let actor = initActorNet(STATE_DIM)
let c = initCriticNet(STATE_DIM, ACTION_DIM)
saveWeights(zipPath, actor, c, c, c, c, 0.2'f32)
check fileExists(zipPath)
check not fileExists(tmpZip)
removeFile(zipPath)
suite "Error handling":
test "loadCheckpoint missing file → IOError":
var raised = false
try:
discard loadCheckpoint(getTempDir() / "nonexistent_xxxxxx.zip")
except IOError:
raised = true
check raised
SAC_LSTM_Bot_garage/tools/.watch_dashboard.lock
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SAC_LSTM_Bot_garage/tools/plot_progress.py
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#!/usr/bin/env python3
"""Build the single self-updating SAC training dashboard.
Pure-stdlib SVG output (matplotlib not available on this box).
Generates ONE file:
docs/campaign_dashboard.svg - four panels, current (v2) run only:
1. test-match win % vs opponents (campaign_v4_stdout.log eval lines)
2. critic_loss / |actor_loss| / alpha (training_metrics.jsonl; losses log
left axis, alpha linear right axis)
3. throughput, games/hour buckets (training_metrics.jsonl 'epoch' deltas;
1 metrics row == one 10-game chunk,
counts match stdout chunk markers)
4. max score per eval cycle (campaign_v4_stdout.log eval blocks;
eval_log.jsonl only ever holds the
LATEST cycle, so history comes from
the stdout log)
plus an embedded JS snippet that reloads the page every 60 s when the SVG is
opened as a top-level document in Chrome.
Usage:
python3 tools/plot_progress.py [campaign_log] [metrics_jsonl] [outdir]
All args optional; defaults relative to the SAC_LSTM_Bot/ root (parent of tools/).
python3 tools/plot_progress.py --selftest # tiny built-in sanity check
"""
import json
import math
import os
import re
import sys
import tempfile
import traceback
from datetime import datetime
from pathlib import Path
import xml.etree.ElementTree as ET
ROOT = Path(__file__).resolve().parent.parent
LOG_FILE = ROOT / "tools" / "plot_progress.log"
def log(msg):
"""Timestamped line to stderr AND tools/plot_progress.log (survives reboots;
the watcher has no terminal to read errors from)."""
line = f"{datetime.now():%F %T} {msg}"
print(line)
try:
with open(LOG_FILE, "a") as fh:
fh.write(line + "\n")
except OSError:
pass
EVAL_RE = re.compile(r">>> \[eval\] win rate: (\d+)/(\d+) \(([\d.]+)%\) vs (\S+)")
EVAL_BLOCK_RE = re.compile(r">>> \[eval\] \d+ deterministic rounds vs (\S+)")
ROUND_RE = re.compile(r"Round \d+/\d+\D+ticks:\d+ score:(\d+) win:")
TREND_WINDOW = 10 # rolling mean shown as the thick trend line (panel 1)
RATE_BUCKET = 20 # metric intervals per throughput bucket (~200 games)
GAMES_PER_ROW = 10 # one training_metrics.jsonl row per 10-round chunk
COLORS = {"Corners": "#d62728", "Crazy": "#1f77b4", "Target": "#2ca02c",
"RamFire": "#ff7f0e", "SacTwin": "#9467bd"}
CRITIC_C, ACTOR_C = "#1f77b4", "#ff7f0e"
ALPHA_C = "#9467bd" # alpha line on the losses panel
RELOAD_JS = ('<script type="text/javascript"><![CDATA[ '
'setTimeout(function(){ location.reload(); }, 60000); ]]></script>')
W, H = 1400, 1850
HEALTH_H = 205 # bottom "Reading the signs" cheat-sheet band
# rows: (header_y, panel_top_y, panel_bottom_y, x_left, x_right)
# panel_top sits low enough to leave room under header+sub for the
# per-panel how-to-read guide (3 italic lines, see panel_guide)
C1_L, C1_R = 70, 697
C2_L, C2_R = 747, 1375
ROWS = {
1: (100, 184, 784, C1_L, C1_R),
2: (100, 184, 784, C2_L, C2_R),
3: (940, 1024, 1494, C1_L, C1_R),
4: (940, 1024, 1494, C2_L, C2_R),
}
PANEL_TITLES = [
"Test matches - win % vs opponents",
"Training losses (log) & alpha (linear)",
"Throughput - games per hour",
"Max score per eval cycle",
]
# per-panel how-to-read notes: (what it shows, axes, which direction is better)
GUIDES = {
1: ("How often the bot wins against each opponent in test battles.",
"X = training progress (match number); Y = win rate, 0-100%.",
"higher is better."),
2: ("How well the brain is learning: losses should fall; alpha sets explore/exploit.",
"X = training progress; left Y (log) = losses; right Y (linear) = alpha.",
"lower losses = better; alpha falls over time as the bot gets confident."),
3: ("How many games per hour the bot trains (its learning speed).",
"X = training progress (10-game chunks); Y = games per hour.",
"higher = faster learning; a steady line beats a spiky one."),
4: ("Best single-round score the bot managed in each test cycle.",
"X = eval cycle number; Y = best score achieved.",
"higher is better; a rising trend means the bot is improving."),
}
# bottom health-check cheat-sheet: one column per subsection; each bullet is
# a tuple of pre-wrapped text lines (first line gets the bullet marker)
SIGNS_COLUMNS = [
("Healthy patterns ✅", [
("Both critic and actor losses trending down over time",),
("Alpha decaying slowly from ~1.0,", "then plateauing — this is normal"),
("Win rates appearing and increasing in the eval panel",),
("Max scores rising in the max-score panel",),
("Alpha plateauing is NOT a problem — it means",
"exploration level is stable"),
]),
("Warning signs ⚠️", [
("Losses exploding (suddenly jumping to", "millions or billions)"),
("Win rates staying at 0% for a long time",
"after the first ~20 eval cycles"),
("Alpha reaching 0 — bot stops exploring entirely", "(gets stuck)"),
("Max scores flatlining (no improvement",
"over many eval cycles)"),
("Any single loss value above 1e6",),
]),
("What each metric means (brief)", [
("Critic loss: how wrong the bot's value",
"estimates are — should go down"),
("Actor loss: how well the bot's action policy",
"is doing — should go down overall", "(some bumps are normal)"),
("Alpha: exploration-exploitation tradeoff — starts",
"high, settles at a positive value (NOT zero)"),
("Max score: best score achieved per eval cycle —",
"rising trend = learning"),
]),
]
# ---------- parsing ----------
def parse_eval_series(path):
"""Return {opponent: [win% per eval, in file order]}."""
series = {}
if not path.is_file():
print(f"[skip] eval log not found: {path}")
return series
for line in path.read_text(errors="replace").splitlines():
m = EVAL_RE.search(line)
if not m:
continue
try:
pct = float(m.group(3))
except ValueError:
continue
series.setdefault(m.group(4), []).append(pct)
return series
def parse_max_scores(path):
"""Return {opponent: [best single-round score per eval cycle, in file order]}.
eval_log.jsonl is atomically overwritten every cycle (sac_train.sh mv), so
per-cycle history only exists in the stdout log: each eval prints a
'>>> [eval] N deterministic rounds vs X' header, then Round/score lines,
closed by the '[eval] win rate' (or crashed / no results) line. Training
rounds share the Round-line format, so they are ignored unless inside a block.
"""
out, cur, best = {}, None, None
if not path.is_file():
print(f"[skip] campaign log not found: {path}")
return out
for line in path.read_text(errors="replace").splitlines():
m = EVAL_BLOCK_RE.search(line)
if m:
cur, best = m.group(1), None
continue
if cur is None:
continue
if "[eval]" in line: # win-rate / crashed / no-results closes the block
if best is not None:
out.setdefault(cur, []).append(best)
cur, best = None, None
continue
m = ROUND_RE.search(line)
if m:
v = int(m.group(1))
best = v if best is None else max(best, v)
return out
def parse_metrics(path):
"""Return list of metric dicts, skipping malformed lines."""
rows = []
if not path.is_file():
print(f"[skip] metrics file not found: {path}")
return rows
for line in path.read_text(errors="replace").splitlines():
line = line.strip()
if not line:
continue
try:
rows.append(json.loads(line))
except json.JSONDecodeError:
continue
return rows
def metric_col(rows, key, positive=False):
"""[(index, value)] for float-parseable rows; abs() applied; optional >0 filter."""
out = []
for i, r in enumerate(rows):
try:
v = abs(float(r[key]))
except (KeyError, TypeError, ValueError):
continue
if positive and v <= 0:
continue
out.append((i, v))
return out
def rolling(vals, w=TREND_WINDOW):
out, s = [], 0.0
for i, v in enumerate(vals):
s += v
if i >= w:
s -= vals[i - w]
out.append(s / min(i + 1, w))
return out
def bucket_means(vals, n):
"""Split vals into <=n contiguous buckets of near-equal size; per-bucket mean."""
if not vals:
return []
n = min(n, len(vals))
k, rem = divmod(len(vals), n)
out, i = [], 0
for b in range(n):
size = k + (1 if b < rem else 0)
out.append(sum(vals[i:i + size]) / size)
i += size
return out
# ---------- tiny SVG helpers ----------
def esc(s):
return str(s).replace("&", "&amp;").replace("<", "&lt;").replace(">", "&gt;")
def write_svg(path, text):
"""Validate the finished SVG, then atomically swap it into place.
Readers never see partial output; an invalid render aborts without
touching the previous good file."""
try:
ET.fromstring(text)
except ET.ParseError as e:
print(f"[error] {path.name}: generated SVG invalid, keeping old file ({e})")
return False
tmp = path.with_name(f"{path.name}.{os.getpid()}.tmp") # unique: a manual
tmp.write_text(text) # run + watcher can race
os.replace(tmp, path)
return True
def polyline(pts, color, width=1.5, dash=None, opacity=1.0):
if len(pts) < 2:
return ""
d = f' stroke-dasharray="{dash}"' if dash else ""
p = " ".join(f"{x:.1f},{y:.1f}" for x, y in pts)
return (f'<polyline points="{p}" fill="none" stroke="{color}" '
f'stroke-width="{width}" opacity="{opacity}"{d}/>\n')
def dots(pts, color, r=2, opacity=0.25):
return "".join(f'<circle cx="{x:.1f}" cy="{y:.1f}" r="{r}" '
f'fill="{color}" opacity="{opacity}"/>\n' for x, y in pts)
def hgrid(x0, x1, ys, color="#dddddd"):
return "".join(f'<line x1="{x0}" y1="{y:.1f}" x2="{x1}" y2="{y:.1f}" '
f'stroke="{color}"/>\n' for y in ys)
def axis(x0, y0, x1, y1, xt, yt, xlabel, ylabel, ylog=False):
"""Draw axes + ticks + labels. xt/yt are (value, px) tick lists."""
s = (f'<line x1="{x0}" y1="{y0}" x2="{x1}" y2="{y0}" stroke="black"/>\n'
f'<line x1="{x0}" y1="{y0}" x2="{x0}" y2="{y1}" stroke="black"/>\n')
for v, px in xt:
s += (f'<line x1="{px:.1f}" y1="{y0}" x2="{px:.1f}" y2="{y0 + 4}" stroke="black"/>\n'
f'<text x="{px:.1f}" y="{y0 + 17}" text-anchor="middle" font-size="11">'
f"{esc(v)}</text>\n")
for v, py in yt:
s += (f'<line x1="{x0 - 4}" y1="{py:.1f}" x2="{x0}" y2="{py:.1f}" stroke="black"/>\n'
f'<text x="{x0 - 7}" y="{py + 4:.1f}" text-anchor="end" font-size="11">'
f"{esc(v)}</text>\n")
s += (f'<text x="{(x0 + x1) // 2}" y="{y0 + 33}" text-anchor="middle" font-size="12">'
f"{esc(xlabel)}</text>\n"
f'<text x="16" y="{(y0 + y1) // 2}" text-anchor="middle" font-size="12" '
f'transform="rotate(-90 16 {(y0 + y1) // 2})">{esc(ylabel)}</text>\n')
return s
def ticks_linear(vmin, vmax, p0, p1, n=5, fmt="{:.0f}"):
return [(fmt.format(vmin + (vmax - vmin) * i / (n - 1)),
p0 + (p1 - p0) * i / (n - 1)) for i in range(n)]
def ticks_log(vmin, vmax, p0, p1, n=5):
vals = [10 ** (vmin + (vmax - vmin) * i / (n - 1)) for i in range(n)]
return [("{:.3g}".format(v), p0 + (p1 - p0) * i / (n - 1)) for i, v in enumerate(vals)]
def legend(items, x, y):
"""items: [(color, label)]"""
s = ""
for i, (color, label) in enumerate(items):
yy = y + i * 18
s += (f'<line x1="{x}" y1="{yy}" x2="{x + 28}" y2="{yy}" stroke="{color}" '
f'stroke-width="3"/>\n'
f'<text x="{x + 34}" y="{yy + 4}" font-size="12">{esc(label)}</text>\n')
return s
def map_fn(p0, p1, vmin, vmax, log=False):
def f(v):
span = (vmax - vmin) or 1 # single-point series -> degenerate range
t = (math.log10(v) - vmin) / span if log else (v - vmin) / span
return p0 + max(0.0, min(1.0, t)) * (p1 - p0)
return f
def signs_block():
"""Health-check cheat-sheet band along the bottom of the canvas."""
y0 = H - HEALTH_H
s = f'<rect x="0" y="{y0}" width="{W}" height="{HEALTH_H}" fill="#f2f2f2"/>\n'
s += (f'<text x="16" y="{y0 + 21}" font-size="15" font-weight="bold">'
"Reading the signs</text>\n"
f'<text x="{W - 16}" y="{y0 + 20}" text-anchor="end" font-size="11" '
'fill="#666">Regenerate anytime: python3 tools/plot_progress.py</text>\n')
for (head, bullets), x in zip(SIGNS_COLUMNS, (16, 500, 985)):
s += (f'<text x="{x}" y="{y0 + 43}" font-size="13" font-weight="bold">'
f"{esc(head)}</text>\n")
y = y0 + 61
for lines in bullets:
for j, ln in enumerate(lines):
s += (f'<text x="{x}" y="{y}" font-size="12">'
f"{esc(('• ' if j == 0 else ' ') + ln)}</text>\n")
y += 14
return s
def header(x, y, title, sub=None):
s = (f'<text x="{x}" y="{y}" font-size="15" font-weight="bold">'
f"{esc(title)}</text>\n")
if sub:
s += (f'<text x="{x}" y="{y + 15}" font-size="11" fill="#555">'
f"{esc(sub)}</text>\n")
return s
def panel_guide(x0, y, guide):
"""Italic how-to-read note between a panel's header and its plot area."""
what, axes, better = guide
s = ""
for i, txt in enumerate((f"What: {what}", f"Axes: {axes}", f"Better: {better}")):
s += (f'<text x="{x0}" y="{y + i * 16}" font-size="12" font-style="italic" '
f'fill="#555">{esc(txt)}</text>\n')
return s
# ---------- panels ----------
def panel_test_matches(series, geo):
_, pt, pb, x0, x1 = geo
s = ""
if not series:
s += f'<text x="{x0 + 10}" y="{pt + 40}" font-size="12" fill="#a00">' \
"no eval lines found</text>\n"
return s
xmax = max(max(len(v) for v in series.values()), 2)
xm, ym = map_fn(x0, x1, 1, xmax), map_fn(pb, pt, 0, 100)
s += hgrid(x0, x1, [ym(v) for v in range(0, 101, 20)])
step = max(1, (xmax // 8 // 10) * 10)
xt = [(str(v), xm(v)) for v in range(step, xmax + 1, step)] or [("1", xm(1))]
s += axis(x0, pb, x1, pt, xt, ticks_linear(0, 100, pb, pt, n=6),
"test match number (each opponent)", "win rate (%)")
items = []
for name in ("Corners", "Crazy", "Target"):
vals = series.get(name, [])
if not vals:
continue
c = COLORS[name]
s += dots([(xm(i + 1), ym(v)) for i, v in enumerate(vals)], c)
s += polyline([(xm(i + 1), ym(v))
for i, v in enumerate(rolling(vals))], c, 3.5)
items.append((c, f"{name} - {len(vals)} evals"))
s += legend(items, x0 + 12, pb + 52)
return s
def panel_max_score(maxes, geo):
_, pt, pb, x0, x1 = geo
s = ""
if not maxes:
s += f'<text x="{x0 + 10}" y="{pt + 40}" font-size="12" fill="#a00">' \
"no eval lines found</text>\n"
return s
ncyc = max(len(v) for v in maxes.values())
xmax = max(ncyc, 2)
hi = max(1, max(v for vals in maxes.values() for v in vals)) * 1.1
xm, ym = map_fn(x0, x1, 1, xmax), map_fn(pb, pt, 0, hi)
s += hgrid(x0, x1, [ym(hi * k / 4) for k in range(5)])
step = max(1, xmax // 8)
xt = [(str(v), xm(v)) for v in range(step, xmax + 1, step)] or [("1", xm(1))]
s += axis(x0, pb, x1, pt, xt, ticks_linear(0, hi, pb, pt, n=5),
"eval cycle number", "best single-round score")
items, per_opp = [], {}
for name in ("Corners", "Crazy", "Target"):
vals = maxes.get(name, [])
if not vals:
continue
c = COLORS[name]
pts = [(xm(i + 1), ym(v)) for i, v in enumerate(vals)]
per_opp[name] = vals
s += dots(pts, c)
s += polyline(pts, c, 2.5)
items.append((c, f"{name} - {len(vals)} evals"))
if len(items) >= 2: # combined best across opponents, cycle-aligned
comb = [max(vals[i] for vals in per_opp.values() if i < len(vals))
for i in range(ncyc)]
s += polyline([(xm(i + 1), ym(v)) for i, v in enumerate(comb)],
"#555555", 2.5, dash="6 4")
items.append(("#555555", "combined max"))
s += legend(items, x0 + 12, pb + 52)
return s
def panel_losses(rows, geo):
_, pt, pb, x0, x1 = geo
s = ""
critic = metric_col(rows, "critic_loss", positive=True)
actor = metric_col(rows, "actor_loss") # abs() applied; sign dropped
actor = [(i, v) for i, v in actor if v > 0]
alpha = metric_col(rows, "alpha")
if not (critic or actor or alpha):
s += f'<text x="{x0 + 10}" y="{pt + 40}" font-size="12" fill="#a00">' \
"no valid loss points</text>\n"
return s
x1 -= 46 # room on the right for the twin alpha axis labels
n = len(rows)
xm = lambda i: x0 + (x1 - x0) * i / max(n - 1, 1)
# log-domain source (losses in practice); drop non-positive values so a
# run of alpha==0 rows can't raise math domain error and kill the build
base = [(i, v) for i, v in critic + actor if v > 0] or \
[(i, v) for i, v in alpha if v > 0]
if not base:
s += f'<text x="{x0 + 10}" y="{pt + 40}" font-size="12" fill="#a00">' \
"no valid loss points</text>\n"
return s
lo = math.floor(math.log10(min(v for _, v in base)))
hi = math.ceil(math.log10(max(v for _, v in base)))
if lo == hi:
hi = lo + 1
ym = map_fn(pb, pt, lo, hi, log=True)
s += hgrid(x0, x1, [ym(10 ** e) for e in range(lo, hi + 1)])
s += axis(x0, pb, x1, pt, ticks_linear(1, n, x0, x1, n=5),
ticks_log(lo, hi, pb, pt), "metric line number", "loss (log)")
s += polyline([(xm(i), ym(v)) for i, v in critic], CRITIC_C, 1.8)
s += polyline([(xm(i), ym(v)) for i, v in actor], ACTOR_C, 1.8)
if alpha: # twin axis: alpha on its own linear scale, purple like the line
ahi = max(1.0, max(v for _, v in alpha))
yma = map_fn(pb, pt, 0, ahi)
s += hgrid(x0, x1, [yma(ahi * k / 4) for k in range(5)], "#e9dcf5")
mid = (pt + pb) // 2
s += f'<line x1="{x1}" y1="{pb}" x2="{x1}" y2="{pt}" stroke="{ALPHA_C}"/>\n'
for v, py in ticks_linear(0, ahi, pb, pt, n=5, fmt="{:.3g}"):
s += (f'<line x1="{x1}" y1="{py:.1f}" x2="{x1 + 4}" y2="{py:.1f}" '
f'stroke="{ALPHA_C}"/>\n'
f'<text x="{x1 + 7}" y="{py + 4:.1f}" font-size="11" '
f'fill="{ALPHA_C}">{esc(v)}</text>\n')
s += (f'<text x="{x1 + 17}" y="{mid}" text-anchor="middle" font-size="12" '
f'fill="{ALPHA_C}" transform="rotate(90 {x1 + 17} {mid})">alpha</text>\n')
s += polyline([(xm(i), yma(v)) for i, v in alpha], ALPHA_C, 1.8)
items = [(CRITIC_C, "critic_loss"), (ACTOR_C, "|actor_loss|")]
if alpha:
items.append((ALPHA_C, "alpha"))
s += legend(items, x0 + 12, pb + 52)
return s
def panel_throughput(rows, geo):
_, pt, pb, x0, x1 = geo
s = ""
eps = []
for r in rows:
try:
eps.append(float(r["epoch"]))
except (KeyError, TypeError, ValueError):
continue
rates = [] # games/hour per inter-row interval
for a, b in zip(eps, eps[1:]):
dt = b - a
if dt > 0:
rates.append(3600.0 * GAMES_PER_ROW / dt)
if not rates:
s += f'<text x="{x0 + 10}" y="{pt + 40}" font-size="12" fill="#a00">' \
"no usable epoch timestamps</text>\n"
return s
bm = bucket_means(rates, RATE_BUCKET)
xm = map_fn(x0, x1, 1, len(rates))
ymax = max(max(rates), max(bm)) * 1.1
ym = map_fn(pb, pt, 0, ymax)
s += hgrid(x0, x1, [ym(ymax * k / 4) for k in range(5)])
step = max(1, len(rates) // 10)
xt = [(str(v), xm(v)) for v in range(step, len(rates) + 1, step)]
s += axis(x0, pb, x1, pt, xt, ticks_linear(0, ymax, pb, pt, n=5, fmt="{:.0f}"),
f"chunk interval ({GAMES_PER_ROW}-game chunks)", "games / hour")
s += dots([(xm(i + 1), ym(v)) for i, v in enumerate(rates)], "#7f7f7f", r=1.6)
if len(bm) >= 2:
ctr = [xm(round((i + 0.5) * len(rates) / len(bm))) for i in range(len(bm))]
s += polyline(list(zip(ctr, map(ym, bm))), "#2ca02c", 3.5)
return s
# ---------- assembly ----------
def build_dashboard(campaign, metrics_f, out):
series = parse_eval_series(campaign)
print("[info] evals parsed: " +
(", ".join(f"{k}={len(v)}" for k, v in sorted(series.items())) or "(none)"))
maxes = parse_max_scores(campaign)
print("[info] eval max-score cycles parsed: " +
(", ".join(f"{k}={len(v)}" for k, v in sorted(maxes.items())) or "(none)"))
rows = parse_metrics(metrics_f)
print(f"[info] metric rows parsed: {len(rows)}")
s = (f'<svg xmlns="http://www.w3.org/2000/svg" width="{W}" height="{H}" '
f'viewBox="0 0 {W} {H}" font-family="sans-serif">\n'
f'<rect width="{W}" height="{H}" fill="white"/>\n'
f'<text x="{W // 2}" y="32" text-anchor="middle" font-size="21" '
f'font-weight="bold">SAC-LSTM campaign dashboard - live run (current only)</text>\n'
f'<text x="{W // 2}" y="56" text-anchor="middle" font-size="12" fill="#555">'
f'generated {datetime.now():%Y-%m-%d %H:%M:%S} - auto-reloads every 60 s '
f'(open this file in Chrome)</text>\n')
drawers = [
(ROWS[1], PANEL_TITLES[0], GUIDES[1],
"raw dots = single test matches, thick = rolling-mean-%d" % TREND_WINDOW,
lambda: panel_test_matches(series, ROWS[1])),
(ROWS[2], PANEL_TITLES[1], GUIDES[2],
"training_metrics.jsonl - big early spikes are normal",
lambda: panel_losses(rows, ROWS[2])),
(ROWS[3], PANEL_TITLES[2], GUIDES[3],
"method: training_metrics.jsonl 'epoch' deltas; 1 row = one 10-game chunk",
lambda: panel_throughput(rows, ROWS[3])),
(ROWS[4], PANEL_TITLES[3], GUIDES[4],
"campaign_v4_stdout.log - max of the 10 deterministic round scores per eval",
lambda: panel_max_score(maxes, ROWS[4])),
]
for geo, title, guide, sub, drawer in drawers:
s += header(geo[3], geo[0], title, sub)
s += panel_guide(geo[3], geo[0] + 33, guide)
try:
body = drawer()
except Exception as e: # one bad panel must not kill the whole page
log(f"[warn] panel '{title}' failed, rendering placeholder: {e}")
body = (f'<text x="{geo[3] + 10}" y="{geo[1] + 40}" font-size="12" '
f'fill="#a00">panel error: {esc(e)}</text>')
s += body or "" # panels bare-return None on their no-data path
s += signs_block()
s += RELOAD_JS + "\n"
s += "</svg>\n"
return write_svg(out, s)
# ---------- selftest ----------
def selftest():
with tempfile.TemporaryDirectory() as td:
td = Path(td)
(td / "log").write_text(
">>> [eval] 10 deterministic rounds vs Corners\n"
"Round 1/10 - ticks:100 score:3 win:true\n"
"garbage line\n"
"Round 2/10 - ticks:100 score:7 win:false\n"
">>> [eval] win rate: 3/10 (30%) vs Corners\n"
">>> [eval] 10 deterministic rounds vs Crazy\n"
"Round 1/10 - ticks:100 score:70 win:false\n"
">>> [eval] win rate: 7/10 (70%) vs Crazy\n"
">>> [eval] broken\n"
"Round 9/9 - ticks:1 score:999 win:false\n"
">>> [eval] 10 deterministic rounds vs Corners\n"
"Round 1/10 - ticks:100 score:50 win:true\n"
">>> [eval] win rate: 5/10 (50%) vs Corners\n"
">>> [eval] 10 deterministic rounds vs Crazy\n"
"Round 1/10 - ticks:100 score:40 win:true\n"
">>> [eval] win rate: 4/10 (40%) vs Crazy\n")
ser = parse_eval_series(td / "log")
assert ser == {"Corners": [30.0, 50.0], "Crazy": [70.0, 40.0]}, ser
mxs = parse_max_scores(td / "log")
# stray Round 999 after the unclosed '[eval] broken' line is ignored;
# per-cycle max of the Round scores above
assert mxs == {"Corners": [7, 50], "Crazy": [70, 40]}, mxs
assert rolling([10] * 25, 20)[-1] == 10.0
assert rolling([1, 2, 3], 20) == [1.0, 1.5, 2.0]
assert len(bucket_means(list(range(1287)), RATE_BUCKET)) == RATE_BUCKET
bm = bucket_means([0, 10], RATE_BUCKET)
assert bm == [0.0, 10.0], bm # fewer points than buckets -> no empty buckets
(td / "m.jsonl").write_text(
'{"epoch": 1000.0, "critic_loss": 10, "actor_loss": -2, "alpha": 0.5}\n'
"not json\n"
'{"epoch": 1060.0, "critic_loss": 100, "actor_loss": -4, "alpha": 0.25}\n'
'{"epoch": 1090.0, "critic_loss": 50, "actor_loss": 3, "alpha": 0.2}\n')
rows = parse_metrics(td / "m.jsonl")
assert len(rows) == 3 and rows[1]["critic_loss"] == 100
assert metric_col(rows, "actor_loss") == [(0, 2.0), (1, 4.0), (2, 3.0)]
dash = td / "dash.svg"
assert build_dashboard(td / "log", td / "m.jsonl", dash)
text = dash.read_text()
ET.fromstring(text) # whole doc must parse -> closing tag present
assert RELOAD_JS in text, "auto-reload script missing"
for t in PANEL_TITLES:
assert esc(t) in text, f"panel title missing: {t}"
assert text.count(PANEL_TITLES[0]) == 1
assert "Reading the signs" in text, "health-check section missing"
for head, bullets in SIGNS_COLUMNS:
assert esc(head) in text, f"health-check column missing: {head}"
for lines in bullets:
assert esc("• " + lines[0]) in text, f"bullet missing: {lines[0]}"
assert text.count("• ") == sum(len(b) for _, b in SIGNS_COLUMNS)
assert 'width="1400"' in text and 'height="1850"' in text
# every panel carries its own What/Axes/Better how-to-read note
assert text.count("What:") == len(PANEL_TITLES), text.count("What:")
for g in GUIDES.values():
assert esc(g[0]) in text, f"panel guide missing: {g[0]}"
# circles: 4 eval dots (panel 1) + 2 throughput rate dots (panel 3)
# + 2 cycles x 2 opponents max-score dots (panel 4)
assert text.count("<circle") == 10, text.count("<circle")
# 2 trends + 3 losses-panel polylines (critic, actor, alpha)
# + 1 throughput
# + 3 max-score panel (Corners, Crazy, combined; Target absent in fixture)
assert text.count("<polyline") == 9, text.count("<polyline")
# losses panel twin axis: "alpha" text = legend + right ylabel; purple
# fills = 5 right-axis tick labels + rotated ylabel
assert text.count(">alpha<") == 2, text.count(">alpha<")
assert text.count('fill="#9467bd"') == 6, text.count('fill="#9467bd"')
assert "combined max" in text, "max-score combined line missing"
# orientation guard: a known rising series (10% -> 90%) rendered through
# the FULL build path must plot upward (smaller SVG y) and forward in
# time (larger x). Fails loudly if axis mapping is ever inverted again.
ori_log = td / "ori.log"
ori_log.write_text(
">>> [eval] win rate: 1/10 (10%) vs Corners\n"
">>> [eval] win rate: 9/10 (90%) vs Corners\n")
ori_dash = td / "ori.svg"
assert build_dashboard(ori_log, td / "m.jsonl", ori_dash)
m = re.search(r'<polyline points="([^"]+)"', ori_dash.read_text())
pts = [tuple(map(float, p.split(","))) for p in m.group(1).split()]
assert len(pts) == 2, pts
(xa, ya), (xb, yb) = pts
assert yb < ya, f"y-axis inverted: win % rose 10->90 but ink moved down ({ya} -> {yb})"
assert xb > xa, f"x-axis reversed: newer eval plotted left ({xa} -> {xb})"
# resilience: missing/empty inputs render placeholders, never crash
empty_dash = td / "empty.svg"
assert build_dashboard(td / "nope.log", td / "nope.jsonl", empty_dash)
etext = empty_dash.read_text()
assert etext.count("no eval lines found") == 2, etext.count("no eval lines found")
assert "no valid loss points" in etext
assert "no usable epoch timestamps" in etext
# all-zero alpha rows (post-crash trainer state) must not raise in the
# losses panel's log-domain math -> placeholder instead of dead build
(td / "zero.jsonl").write_text(
'{"epoch": 1000.0, "alpha": 0}\n{"epoch": 1060.0, "alpha": 0}\n')
zero_dash = td / "zero.svg"
assert build_dashboard(td / "log", td / "zero.jsonl", zero_dash)
assert "no valid loss points" in zero_dash.read_text()
# single throughput interval (fresh 2-row metrics file right after a
# restart) used to divide by zero in map_fn and kill the whole build
(td / "one.jsonl").write_text(
'{"epoch": 1000.0, "critic_loss": 10, "alpha": 0.5}\n'
'{"epoch": 1060.0, "critic_loss": 5, "alpha": 0.4}\n')
one_dash = td / "one.svg"
assert build_dashboard(td / "log", td / "one.jsonl", one_dash)
assert "panel error" not in one_dash.read_text()
assert "<polyline" in one_dash.read_text()
print("selftest OK")
def main():
if "--selftest" in sys.argv:
selftest()
return
args = [a for a in sys.argv[1:] if not a.startswith("-")]
campaign = Path(args[0]) if len(args) > 0 else ROOT / "campaign_v4_stdout.log"
metrics = Path(args[1]) if len(args) > 1 else ROOT / "training_metrics.jsonl"
outdir = Path(args[2]) if len(args) > 2 else ROOT / "docs"
try:
outdir.mkdir(parents=True, exist_ok=True)
for p in (campaign, metrics):
if not p.is_file():
# loudest symptom of a watcher launched from a stale checkout
log(f"[warn] input missing: {p} - is this the live checkout?")
ok = build_dashboard(campaign, metrics, outdir / "campaign_dashboard.svg")
except Exception:
log(f"[error] dashboard build failed:\n{traceback.format_exc().rstrip()}")
ok = False
if ok:
print(f"[done] dashboard written to {outdir / 'campaign_dashboard.svg'}")
else:
print("[error] dashboard NOT updated - see " + str(LOG_FILE))
sys.exit(1)
if __name__ == "__main__":
main()
SAC_LSTM_Bot_garage/tools/watch_dashboard.sh
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#!/bin/sh
# Keep docs/campaign_dashboard.svg fresh: regenerate every 60 s.
# Failures are appended to tools/plot_progress.log (by this wrapper and by
# plot_progress.py itself) - check that file when the dashboard looks stale.
# The startup banner records WHICH tree this instance watches, so a copy
# launched from a stale checkout (the post-reboot failure mode) is visible.
set -u
dir=$(cd "$(dirname "$0")" && pwd)
log="$dir/plot_progress.log"
if command -v flock >/dev/null 2>&1; then
exec 9>"$dir/.watch_dashboard.lock"
flock -n 9 || { echo "[watch_dashboard] another instance already running, exiting" >&2; exit 0; }
fi
echo "[watch_dashboard] $(date '+%F %T') started, watching root=$(cd "$dir/.." && pwd)" >> "$log" 2>/dev/null || true
while :; do
if ! python3 "$dir/plot_progress.py"; then
echo "[watch_dashboard] $(date '+%F %T') regeneration failed (see $log)" >&2
echo "[watch_dashboard] $(date '+%F %T') regeneration failed" >> "$log" 2>/dev/null || true
fi
sleep 60
done
SAC_LSTM_Bot_garage/training_metrics.jsonl.pre-merge
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{"epoch":1787565804.965847,"steps":1,"buffer_size":24,"drained":1,"grad_steps":1,"critic_loss":6.929736,"actor_loss":13.433716,"alpha_loss":-0.0044489726,"alpha":0.9753562}
{"epoch":1787565814.1075847,"steps":1,"buffer_size":24,"drained":1,"grad_steps":1,"critic_loss":0.05124796,"actor_loss":12.885783,"alpha_loss":0.016930042,"alpha":0.9762861}
{"epoch":1787565961.245973,"steps":1,"buffer_size":24,"drained":1,"grad_steps":1,"critic_loss":0.4263701,"actor_loss":14.863115,"alpha_loss":0.03347406,"alpha":0.97734594}
{"epoch":1787565971.2967772,"steps":1,"buffer_size":24,"drained":1,"grad_steps":1,"critic_loss":0.30882663,"actor_loss":15.45638,"alpha_loss":0.047085714,"alpha":0.97881573}
{"epoch":1787566172.4240468,"steps":1,"buffer_size":24,"drained":1,"grad_steps":1,"critic_loss":3.1835616,"actor_loss":22.56434,"alpha_loss":0.117146604,"alpha":0.9814878}
{"epoch":1787566184.1664138,"steps":1,"buffer_size":24,"drained":1,"grad_steps":1,"critic_loss":2.150265,"actor_loss":22.027954,"alpha_loss":0.07355834,"alpha":0.98294044}
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