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SirStone/SirRoboGarage:master SirStone/SirRoboGarage:devcontrolbot SirStone/SirRoboGarage:research/lead-targeting SirStone/SirRoboGarage:evo-bot SirStone/SirRoboGarage:research/ga-parameters SirStone/SirRoboGarage:worktree-agent-a393a8b9
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compare: SirStone/SirRoboGarage:8eb7c53dd0d11dc65cde79e9e96f183e80d572c9
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SirStone/SirRoboGarage:devcontrolbot SirStone/SirRoboGarage:master SirStone/SirRoboGarage:research/lead-targeting SirStone/SirRoboGarage:evo-bot SirStone/SirRoboGarage:research/ga-parameters SirStone/SirRoboGarage:worktree-agent-a393a8b9

11 Commits
research/ga-parameters ... 8eb7c53dd0

Author SHA1 Message Date
SirStone 8eb7c53dd0 fix(dashboard): remove broken unused win%-per-opponent panel 2026-08-24 09:05:41 +02:00
SirStone 51e7f33ecb fix(dashboard): plot live campaign-4 logs; derive ROOT from script location 2026-08-24 08:30:04 +02:00
SirStone d2d205e6f6 Merge branch 'research/ga-parameters' into research/goto-controller 2026-08-24 08:23:25 +02:00
SirStone 3851f28cc5 Merge branch 'worktree-agent-a556517b' into research/goto-controller 2026-08-24 08:23:22 +02:00
SirStone 6241c41e52 Merge branch 'worktree-agent-a4c06ae3' into research/goto-controller 2026-08-24 08:23:19 +02:00
SirStone 156b4ae8db Merge branch 'worktree-agent-af671e69' into research/goto-controller 2026-08-24 08:23:12 +02:00
SirStone eee48dec38 prototype: GA evolution spike -- sin(x) prediction validates pipeline (#66) 
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
 2026-08-24 08:14:36 +02:00
SirStone eb8faa7c99 prototype: GA evolution spike -- sin(x) prediction validates pipeline (#66) 
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
 2026-08-24 08:14:32 +02:00
SirStone cb1bbc35dc docs(adr): Evo_Bot neuroevolution gun architecture (#63) 
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
 2026-08-24 00:02:47 +02:00
SirStone b7b10af811 feat: OscillatorBot sparring partner for GA gun testing (#64) 
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
 2026-08-24 00:01:38 +02:00
SirStone e455566c8e docs: CONTEXT.md — Evo_Bot domain vocabulary (#62) 
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
 2026-08-24 00:00:21 +02:00
9 changed files with 466 additions and 779 deletions
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CONTEXT.md
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# Evo_Bot
Robocode Tank Royale bot with a modular gun system where evolved neural networks learn to predict enemy dodge behavior.
## Language
**Evo_Bot**:
The bot itself — handles movement and firing discipline. Guns are pluggable modules.
_Avoid_: robot, tank
**Guess Factor (GF)**:
A value from -1 to +1 representing where on the maximum escape angle arc the enemy is. 0 = directly ahead, -1 = full left dodge, +1 = full right dodge. The gun's prediction target.
_Avoid_: aim offset, dodge index
**Max Escape Angle (MEA)**:
The widest angle the enemy can reach before a bullet arrives, computed from distance and bullet speed. Guess factor is multiplied by MEA to get the aim offset.
**Lateral Velocity**:
Enemy speed projected perpendicular to the line between you and them. The primary signal for guess factor prediction.
_Avoid_: tangential speed, sideways velocity
**Sliding Window**:
The last N ticks (default 30) of enemy state fed as input to the network. Each tick contains lateral velocity, heading delta, and wall distance ahead.
_Avoid_: observation buffer, input history
**Replay Tape**:
Rolling buffer of recorded enemy states (~2000 ticks). The evolution thread evaluates gun fitness against this tape.
_Avoid_: experience buffer, replay buffer
**Virtual Gun**:
A gun that runs in parallel without actually firing. It tracks where it would have aimed and whether a simulated bullet would have hit. Used to compare gun variants.
**Virtual Bullet**:
A simulated bullet fired by a virtual gun. Never actually sent to the game engine.
**TOPO_Gun**:
Fixed-topology ANN gun evolved by GA. Network shape is predetermined (e.g., 91-8-1), only weights are evolved.
_Avoid_: static gun, fixed gun
**NEAT_Gun**:
Variable-topology ANN gun where evolution can add/remove neurons and connections (NEAT algorithm). Deferred — only built if TOPO_Gun hits a ceiling.
**Population**:
The set of candidate networks (default 64-200) being evolved. Each member is a complete set of ANN weights.
**Champion**:
The best-performing network in the current GA population. The champion's weights are pushed to the inference side when it beats the current best.
_Avoid_: best, winner, elite
**Fitness**:
Hit count when a network's aim predictions are evaluated as virtual bullets against sampled ticks from the replay tape.
_Avoid_: score, reward
**Cold Start**:
The first-ever battle with no saved weights. The gun does not fire until the evolution thread produces its first champion. Subsequent battles load persisted weights.
**Weight Persistence**:
Saving evolved weights to disk. Load order: per-opponent file, then global fallback, then random initialization.
_Avoid_: model saving, checkpointing
OscillatorBot/OscillatorBot.json
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{
"name": "OscillatorBot",
"version": "0.1.0",
"authors": ["Davide Cappellini"],
"description": "Predictable zigzag sparring partner for gun testing",
"homepage": "",
"countryCodes": ["IT"],
"gameTypes": ["classic", "1v1"],
"platform": "Nim",
"programmingLang": "Nim"
}
OscillatorBot/OscillatorBot.nim
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# OscillatorBot — predictable zigzag sparring partner for GA gun testing.
# Reverses direction + turn every PERIOD ticks. Head-on targeting only.
import std/[math, os]
import tankroyale_botapi
const botJsonPath = currentSourcePath().parentDir / "OscillatorBot.json"
const
SPEED = 7.0 # forward/backward speed
PERIOD = 25 # ticks between direction reversals
type OscillatorBot = ref object of Bot
tickCount: int
moveSign: float # +1 forward, -1 backward
turnSign: float # +1 right, -1 left
method onRoundStarted*(bot: OscillatorBot, e: RoundStartedEvent) =
setAdjustGunForBodyTurn(true)
setAdjustRadarForBodyTurn(true)
setAdjustRadarForGunTurn(true)
bot.tickCount = 0
bot.moveSign = 1.0
bot.turnSign = 1.0
method onScannedBot*(bot: OscillatorBot, e: ScannedBotEvent) =
# Head-on targeting: aim gun directly at enemy, fire medium power
let bearing = directionTo(getX(), getY(), e.x, e.y)
let gunDelta = normalizeRelativeAngle(bearing - getGunDirection())
setGunTurnRate(gunDelta.clamp(-MAX_GUN_TURN_RATE, MAX_GUN_TURN_RATE))
if abs(gunDelta) < 10.0 and getGunHeat() <= 0.0:
discard setFire(2.0)
method run*(bot: OscillatorBot) =
while isRunning():
inc bot.tickCount
if bot.tickCount mod PERIOD == 0:
bot.moveSign *= -1.0
bot.turnSign *= -1.0
setTargetSpeed(bot.moveSign * SPEED)
setTurnRate(bot.turnSign * 4.0)
setRadarTurnRate(45.0) # spin radar to keep scanning
go()
when isMainModule:
var bot = OscillatorBot(moveSign: 1.0, turnSign: 1.0)
start(bot, botJsonPath)
OscillatorBot/OscillatorBot.sh
Executable
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#!/bin/sh
cd "$(dirname "$0")"
exec ./OscillatorBot 2>> /tmp/oscillatorbot_stderr.log
OscillatorBot/config.nims
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--path:"../libs"
SAC_LSTM_Bot/docs/campaign_dashboard.svg
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<svg xmlns="http://www.w3.org/2000/svg" width="1400" height="2000" viewBox="0 0 1400 2000" font-family="sans-serif"> <svg xmlns="http://www.w3.org/2000/svg" width="1400" height="1720" viewBox="0 0 1400 1720" font-family="sans-serif">
<rect width="1400" height="2000" fill="white"/> <rect width="1400" height="1720" 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="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 2026-08-23 10:46:52 - auto-reloads every 60 s (open this file in Chrome)</text> <text x="700" y="56" text-anchor="middle" font-size="12" fill="#555">generated 2026-08-24 09:05:32 - 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="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="115" font-size="11" fill="#555">raw dots = single test matches, thick = rolling-mean-10</text>
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<text x="383" y="753" text-anchor="middle" font-size="12">test match number (each opponent)</text> <text x="383" y="753" text-anchor="middle" font-size="12">test match number (each opponent)</text>
<text x="16" y="420" text-anchor="middle" font-size="12" transform="rotate(-90 16 420)">win rate (%)</text> <text x="16" y="420" text-anchor="middle" font-size="12" transform="rotate(-90 16 420)">win rate (%)</text>
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<text x="70" y="1600" font-size="15" font-weight="bold">Throughput - games per hour</text> <circle cx="781.9" cy="1413.4" r="1.6" fill="#7f7f7f" opacity="0.25"/>
<text x="70" y="1615" font-size="11" fill="#555">method: training_metrics.jsonl 'epoch' deltas (training_log.jsonl has no timestamps); 1 row = one 10-game chunk</text> <circle cx="816.8" cy="1148.0" r="1.6" fill="#7f7f7f" opacity="0.25"/>
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<text x="850.4" y="1797" text-anchor="middle" font-size="11">120</text> <text x="16" y="1561" font-size="15" font-weight="bold">How to read</text>
<line x1="981.5" y1="1780" x2="981.5" y2="1784" stroke="black"/> <text x="1384" y="1560" text-anchor="end" font-size="11" fill="#666">Regenerate anytime: python3 tools/plot_progress.py</text>
<text x="981.5" y="1797" text-anchor="middle" font-size="11">140</text> <text x="16" y="1583" font-size="13">Test matches: dots are single fights, thick line shows trend.</text>
<line x1="1112.7" y1="1780" x2="1112.7" y2="1784" stroke="black"/> <text x="16" y="1600" font-size="13">Loss spikes are normal early; endless growth is bad.</text>
<text x="1112.7" y="1797" text-anchor="middle" font-size="11">160</text> <text x="16" y="1617" font-size="13">Alpha high means experimenting; falling too fast freezes habits.</text>
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<text x="1243.8" y="1797" text-anchor="middle" font-size="11">180</text> <text x="16" y="1651" font-size="13">This file reloads itself in Chrome every sixty seconds.</text>
<line x1="1375.0" y1="1780" x2="1375.0" y2="1784" stroke="black"/> <text x="16" y="1668" font-size="13">Regenerate anytime with tools/watch_dashboard.sh or the python command.</text>
<text x="1375.0" y="1797" text-anchor="middle" font-size="11">200</text>
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<text x="63" y="1622.0" text-anchor="end" font-size="11">6750</text>
<text x="722" y="1813" text-anchor="middle" font-size="12">chunk interval (10-game chunks)</text>
<text x="16" y="1699" text-anchor="middle" font-size="12" transform="rotate(-90 16 1699)">games / hour</text>
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<text x="16" y="1841" font-size="15" font-weight="bold">How to read</text>
<text x="1384" y="1840" text-anchor="end" font-size="11" fill="#666">Regenerate anytime: python3 tools/plot_progress.py</text>
<text x="16" y="1863" font-size="13">Test matches: dots are single fights, thick line shows trend.</text>
<text x="16" y="1880" font-size="13">Real battles only. Rising lines mean the bot improves.</text>
<text x="16" y="1897" font-size="13">Loss spikes are normal early; endless growth is bad.</text>
<text x="16" y="1914" font-size="13">Alpha high means experimenting; falling too fast freezes habits.</text>
<text x="16" y="1931" font-size="13">Throughput flat is healthy; dips mean something slowed.</text>
<text x="16" y="1948" font-size="13">This file reloads itself in Chrome every sixty seconds.</text>
<text x="16" y="1965" font-size="13">Regenerate anytime with tools/watch_dashboard.sh or the python command.</text>
<script type="text/javascript"><![CDATA[ setTimeout(function(){ location.reload(); }, 60000); ]]></script> <script type="text/javascript"><![CDATA[ setTimeout(function(){ location.reload(); }, 60000); ]]></script>
</svg> </svg>
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SAC_LSTM_Bot/tools/plot_progress.py
+25 -115
View File
@@ -3,21 +3,18 @@
Pure-stdlib SVG output (matplotlib not available on this box). Pure-stdlib SVG output (matplotlib not available on this box).
Generates ONE file: Generates ONE file:
docs/campaign_dashboard.svg - five panels, current (v2) run only: docs/campaign_dashboard.svg - four panels, current (v2) run only:
1. test-match win % vs opponents (campaign_v2_stdout.log eval lines) 1. test-match win % vs opponents (campaign_v4_stdout.log eval lines)
2. real-fight win % per opponent (training_log.jsonl, ~100-game buckets) 2. critic_loss / |actor_loss| (training_metrics.jsonl, shared log-y)
3. critic_loss / |actor_loss| (training_metrics.jsonl, shared log-y) 3. alpha temperature (training_metrics.jsonl, linear)
4. alpha temperature (training_metrics.jsonl, linear) 4. throughput, games/hour buckets (training_metrics.jsonl 'epoch' deltas;
5. throughput, games/hour buckets (training_metrics.jsonl 'epoch' deltas; 1 metrics row == one 10-game chunk,
training_log.jsonl has NO timestamps - counts match stdout chunk markers)
verified field names - and one metrics
row == one 10-game chunk, counts match
the stdout "=== Chunk N/N ===" markers)
plus an embedded JS snippet that reloads the page every 60 s when the SVG is plus an embedded JS snippet that reloads the page every 60 s when the SVG is
opened as a top-level document in Chrome. opened as a top-level document in Chrome.
Usage: Usage:
python3 tools/plot_progress.py [campaign_log] [metrics_jsonl] [games_jsonl] [outdir] python3 tools/plot_progress.py [campaign_log] [metrics_jsonl] [outdir]
All args optional; defaults relative to the SAC_LSTM_Bot/ root (parent of tools/). 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 python3 tools/plot_progress.py --selftest # tiny built-in sanity check
""" """
@@ -34,7 +31,6 @@ import xml.etree.ElementTree as ET
ROOT = Path(__file__).resolve().parent.parent ROOT = Path(__file__).resolve().parent.parent
EVAL_RE = re.compile(r">>> \[eval\] win rate: (\d+)/(\d+) \(([\d.]+)%\) vs (\S+)") EVAL_RE = re.compile(r">>> \[eval\] win rate: (\d+)/(\d+) \(([\d.]+)%\) vs (\S+)")
TREND_WINDOW = 10 # rolling mean shown as the thick trend line (panel 1) TREND_WINDOW = 10 # rolling mean shown as the thick trend line (panel 1)
GAME_BUCKET = 100 # games per bucket, real-fight panel
RATE_BUCKET = 20 # metric intervals per throughput bucket (~200 games) RATE_BUCKET = 20 # metric intervals per throughput bucket (~200 games)
GAMES_PER_ROW = 10 # one training_metrics.jsonl row per 10-round chunk GAMES_PER_ROW = 10 # one training_metrics.jsonl row per 10-round chunk
COLORS = {"Corners": "#d62728", "Crazy": "#1f77b4", "Target": "#2ca02c", COLORS = {"Corners": "#d62728", "Crazy": "#1f77b4", "Target": "#2ca02c",
@@ -42,7 +38,7 @@ COLORS = {"Corners": "#d62728", "Crazy": "#1f77b4", "Target": "#2ca02c",
CRITIC_C, ACTOR_C = "#1f77b4", "#ff7f0e" CRITIC_C, ACTOR_C = "#1f77b4", "#ff7f0e"
RELOAD_JS = ('<script type="text/javascript"><![CDATA[ ' RELOAD_JS = ('<script type="text/javascript"><![CDATA[ '
'setTimeout(function(){ location.reload(); }, 60000); ]]></script>') 'setTimeout(function(){ location.reload(); }, 60000); ]]></script>')
W, H = 1400, 2000 W, H = 1400, 1720
TITLE_H, GUIDE_H = 80, 180 TITLE_H, GUIDE_H = 80, 180
# rows: (header_y, panel_top_y, panel_bottom_y, x_left, x_right) # rows: (header_y, panel_top_y, panel_bottom_y, x_left, x_right)
C1_L, C1_R = 70, 697 C1_L, C1_R = 70, 697
@@ -52,18 +48,15 @@ ROWS = {
2: (100, 120, 720, C2_L, C2_R), 2: (100, 120, 720, C2_L, C2_R),
3: (940, 958, 1428, C1_L, C1_R), 3: (940, 958, 1428, C1_L, C1_R),
4: (940, 958, 1428, C2_L, C2_R), 4: (940, 958, 1428, C2_L, C2_R),
5: (1600, 1618, 1780, C1_L, C2_R),
} }
PANEL_TITLES = [ PANEL_TITLES = [
"Test matches - win % vs opponents", "Test matches - win % vs opponents",
"Real fights - win % per opponent",
"Training losses (log scale)", "Training losses (log scale)",
"Alpha temperature", "Alpha temperature",
"Throughput - games per hour", "Throughput - games per hour",
] ]
GUIDE_LINES = [ GUIDE_LINES = [
"Test matches: dots are single fights, thick line shows trend.", "Test matches: dots are single fights, thick line shows trend.",
"Real battles only. Rising lines mean the bot improves.",
"Loss spikes are normal early; endless growth is bad.", "Loss spikes are normal early; endless growth is bad.",
"Alpha high means experimenting; falling too fast freezes habits.", "Alpha high means experimenting; falling too fast freezes habits.",
"Throughput flat is healthy; dips mean something slowed.", "Throughput flat is healthy; dips mean something slowed.",
@@ -109,25 +102,6 @@ def parse_metrics(path):
return rows return rows
def parse_games(path):
"""Return [(opponent, won_bool)] for type=='game' rows, skipping junk."""
out = []
if not path.is_file():
print(f"[skip] games log not found: {path}")
return out
for line in path.read_text(errors="replace").splitlines():
try:
r = json.loads(line)
except json.JSONDecodeError:
continue
if r.get("type") != "game":
continue
opp, win = r.get("opponent"), r.get("win")
if isinstance(opp, str) and isinstance(win, bool):
out.append((opp, win))
return out
def metric_col(rows, key, positive=False): def metric_col(rows, key, positive=False):
"""[(index, value)] for float-parseable rows; abs() applied; optional >0 filter.""" """[(index, value)] for float-parseable rows; abs() applied; optional >0 filter."""
out = [] out = []
@@ -305,53 +279,6 @@ def panel_test_matches(series, geo):
s += legend(items, x0 + 12, pb + 52) s += legend(items, x0 + 12, pb + 52)
return s return s
def panel_real_fights(games, geo):
_, pt, pb, x0, x1 = geo
s = ""
if not games:
s += f'<text x="{x0 + 10}" y="{pt + 40}" font-size="12" fill="#a00">' \
"no game rows found</text>\n"
return
edges = list(range(0, len(games) + 1, GAME_BUCKET))
if edges[-1] != len(games):
edges.append(len(games))
buckets = list(zip(edges[:-1], edges[1:]))
xm, ym = map_fn(x0, x1, 1, max(len(games), 2)), map_fn(pb, pt, 0, 100)
s += hgrid(x0, x1, [ym(v) for v in range(0, 101, 20)])
step = max(GAME_BUCKET, GAME_BUCKET * (len(games) // GAME_BUCKET // 8 + 1))
xt = [(str(v), xm(v)) for v in range(step, len(games) + 1, step)]
s += axis(x0, pb, x1, pt, xt, ticks_linear(0, 100, pb, pt, n=6),
f"game number ({GAME_BUCKET}-game buckets)", "win %")
opponents = []
for opp, _ in games:
if opp not in opponents:
opponents.append(opp)
items = []
for name in opponents:
c = COLORS.get(name, "#7f7f7f")
by_b = {}
for bi, (lo, hi) in enumerate(buckets):
sub = [w for o, w in games[lo:hi] if o == name]
if sub:
by_b[bi] = 100.0 * sum(sub) / len(sub)
pts = []
segs, prev = [], None
for bi in sorted(by_b):
if prev is not None and bi != prev + 1:
segs.append(pts)
pts = []
center = (buckets[bi][0] + buckets[bi][1]) / 2
pts.append((xm(center), ym(by_b[bi])))
prev = bi
if len(pts) >= 2:
segs.append(pts)
for seg in segs:
s += polyline(seg, c, 3.5)
n_played = sum(1 for o, _ in games if o == name)
items.append((c, f"{name} ({n_played} games)"))
s += legend(items, x0 + 12, pb + 52)
return s
def panel_losses(rows, geo): def panel_losses(rows, geo):
_, pt, pb, x0, x1 = geo _, pt, pb, x0, x1 = geo
s = "" s = ""
@@ -434,14 +361,12 @@ def panel_throughput(rows, geo):
# ---------- assembly ---------- # ---------- assembly ----------
def build_dashboard(campaign, metrics_f, games_f, out): def build_dashboard(campaign, metrics_f, out):
series = parse_eval_series(campaign) series = parse_eval_series(campaign)
print("[info] evals parsed: " + print("[info] evals parsed: " +
(", ".join(f"{k}={len(v)}" for k, v in sorted(series.items())) or "(none)")) (", ".join(f"{k}={len(v)}" for k, v in sorted(series.items())) or "(none)"))
rows = parse_metrics(metrics_f) rows = parse_metrics(metrics_f)
print(f"[info] metric rows parsed: {len(rows)}") print(f"[info] metric rows parsed: {len(rows)}")
games = parse_games(games_f)
print(f"[info] game rows parsed: {len(games)}")
s = (f'<svg xmlns="http://www.w3.org/2000/svg" width="{W}" height="{H}" ' 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'viewBox="0 0 {W} {H}" font-family="sans-serif">\n'
@@ -457,18 +382,14 @@ def build_dashboard(campaign, metrics_f, games_f, out):
"raw dots = single test matches, thick = rolling-mean-%d" % TREND_WINDOW, "raw dots = single test matches, thick = rolling-mean-%d" % TREND_WINDOW,
lambda: panel_test_matches(series, ROWS[1])), lambda: panel_test_matches(series, ROWS[1])),
(ROWS[2], PANEL_TITLES[1], (ROWS[2], PANEL_TITLES[1],
"training_log.jsonl only - learning in REAL battles, not tests",
lambda: panel_real_fights(games, ROWS[2])),
(ROWS[3], PANEL_TITLES[2],
"training_metrics.jsonl - big early spikes are normal", "training_metrics.jsonl - big early spikes are normal",
lambda: panel_losses(rows, ROWS[3])), lambda: panel_losses(rows, ROWS[2])),
(ROWS[4], PANEL_TITLES[3], (ROWS[3], PANEL_TITLES[2],
"training_metrics.jsonl - high = exploring, low = exploiting", "training_metrics.jsonl - high = exploring, low = exploiting",
lambda: panel_alpha(rows, ROWS[4])), lambda: panel_alpha(rows, ROWS[3])),
(ROWS[5], PANEL_TITLES[4], (ROWS[4], PANEL_TITLES[3],
"method: training_metrics.jsonl 'epoch' deltas (training_log.jsonl has " "method: training_metrics.jsonl 'epoch' deltas; 1 row = one 10-game chunk",
"no timestamps); 1 row = one 10-game chunk", lambda: panel_throughput(rows, ROWS[4])),
lambda: panel_throughput(rows, ROWS[5])),
] ]
for geo, title, sub, drawer in drawers: for geo, title, sub, drawer in drawers:
s += header(geo[3], geo[0], title, sub) s += header(geo[3], geo[0], title, sub)
@@ -506,20 +427,10 @@ def selftest():
'{"epoch": 1090.0, "critic_loss": 50, "actor_loss": 3, "alpha": 0.2}\n') '{"epoch": 1090.0, "critic_loss": 50, "actor_loss": 3, "alpha": 0.2}\n')
rows = parse_metrics(td / "m.jsonl") rows = parse_metrics(td / "m.jsonl")
assert len(rows) == 3 and rows[1]["critic_loss"] == 100 assert len(rows) == 3 and rows[1]["critic_loss"] == 100
glines = []
for i in range(150): # 2 full GAME_BUCKETs, both opponents in both
glines.append(json.dumps(
{"type": "game", "round": i % 10 + 1, "ticks": 100,
"score": i % 3, "total_score": i, "win": i % 3 == 0,
"opponent": ("Corners", "Crazy")[i % 2]}))
(td / "g.jsonl").write_text("\n".join(glines) + "\n")
games = parse_games(td / "g.jsonl")
assert len(games) == 150 and games[0] == ("Corners", True)
assert games[-1] == ("Crazy", False) # i=149: odd -> Crazy; 149%3!=0 -> loss
assert metric_col(rows, "actor_loss") == [(0, 2.0), (1, 4.0), (2, 3.0)] assert metric_col(rows, "actor_loss") == [(0, 2.0), (1, 4.0), (2, 3.0)]
dash = td / "dash.svg" dash = td / "dash.svg"
assert build_dashboard(td / "log", td / "m.jsonl", td / "g.jsonl", dash) assert build_dashboard(td / "log", td / "m.jsonl", dash)
text = dash.read_text() text = dash.read_text()
ET.fromstring(text) # whole doc must parse -> closing tag present ET.fromstring(text) # whole doc must parse -> closing tag present
assert RELOAD_JS in text, "auto-reload script missing" assert RELOAD_JS in text, "auto-reload script missing"
@@ -527,11 +438,11 @@ def selftest():
assert t in text, f"panel title missing: {t}" assert t in text, f"panel title missing: {t}"
assert text.count(PANEL_TITLES[0]) == 1 assert text.count(PANEL_TITLES[0]) == 1
assert "How to read" in text, "reading guide missing" assert "How to read" in text, "reading guide missing"
assert 'width="1400"' in text and 'height="2000"' in text assert 'width="1400"' in text and 'height="1720"' in text
# circles: 4 eval dots (panel 1) + 2 throughput rate dots (panel 5) # circles: 4 eval dots (panel 1) + 2 throughput rate dots (panel 4)
assert text.count("<circle") == 6, text.count("<circle") assert text.count("<circle") == 6, text.count("<circle")
# 2 trends + 2 real-fight opp lines + 2 losses + 1 alpha + 1 throughput # 2 trends + 2 losses + 1 alpha + 1 throughput
assert text.count("<polyline") == 8, text.count("<polyline") assert text.count("<polyline") == 6, text.count("<polyline")
# orientation guard: a known rising series (10% -> 90%) rendered through # orientation guard: a known rising series (10% -> 90%) rendered through
# the FULL build path must plot upward (smaller SVG y) and forward in # the FULL build path must plot upward (smaller SVG y) and forward in
@@ -541,7 +452,7 @@ def selftest():
">>> [eval] win rate: 1/10 (10%) vs Corners\n" ">>> [eval] win rate: 1/10 (10%) vs Corners\n"
">>> [eval] win rate: 9/10 (90%) vs Corners\n") ">>> [eval] win rate: 9/10 (90%) vs Corners\n")
ori_dash = td / "ori.svg" ori_dash = td / "ori.svg"
assert build_dashboard(ori_log, td / "m.jsonl", td / "g.jsonl", ori_dash) assert build_dashboard(ori_log, td / "m.jsonl", ori_dash)
m = re.search(r'<polyline points="([^"]+)"', ori_dash.read_text()) m = re.search(r'<polyline points="([^"]+)"', ori_dash.read_text())
pts = [tuple(map(float, p.split(","))) for p in m.group(1).split()] pts = [tuple(map(float, p.split(","))) for p in m.group(1).split()]
assert len(pts) == 2, pts assert len(pts) == 2, pts
@@ -556,14 +467,13 @@ def main():
selftest() selftest()
return return
args = [a for a in sys.argv[1:] if not a.startswith("-")] args = [a for a in sys.argv[1:] if not a.startswith("-")]
campaign = Path(args[0]) if len(args) > 0 else ROOT / "campaign_v2_stdout.log" 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" metrics = Path(args[1]) if len(args) > 1 else ROOT / "training_metrics.jsonl"
games = Path(args[2]) if len(args) > 2 else ROOT / "training_log.jsonl" outdir = Path(args[2]) if len(args) > 2 else ROOT / "docs"
outdir = Path(args[3]) if len(args) > 3 else ROOT / "docs"
outdir.mkdir(parents=True, exist_ok=True) outdir.mkdir(parents=True, exist_ok=True)
try: try:
ok = build_dashboard(campaign, metrics, games, outdir / "campaign_dashboard.svg") ok = build_dashboard(campaign, metrics, outdir / "campaign_dashboard.svg")
except Exception as e: except Exception as e:
print(f"[error] dashboard build failed: {e}") print(f"[error] dashboard build failed: {e}")
ok = False ok = False
docs/adr/0001-neuroevolution-gun-fixed-topology-ann-evolved-by-ga.md
+46
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@@ -0,0 +1,46 @@
# Neuroevolution gun with fixed-topology ANN evolved by GA
Evo_Bot needs a gun that adapts to each opponent's dodge patterns during a match, finds nonlinear movement patterns that histograms miss, and is original. We chose a fixed-topology feedforward ANN (91->8->1, 745 weights) whose weights are evolved by a mutation-only GA running on a parallel thread. This beats the alternatives (RL too slow to adapt in-match, Q-learning collapses to histogram for single-shot decisions, guess-factor histograms are unoriginal, transformer/LLM-style prediction is data-starved at ~14k ticks per match) while keeping implementation risk low by deferring topology evolution (NEAT) until the fixed network hits its ceiling.
## Considered Options
- **PPO / SAC (end-to-end RL):** Too slow -- thousands of rounds to converge, cannot adapt mid-match. Explored in other bots in this repo.
- **Q-learning for aiming:** Collapses to a histogram. Single-shot aiming has no sequential decision structure for Q-learning to exploit.
- **Guess-factor histogram:** Proven and fast to converge (~15 ticks), but unoriginal -- 20 years of community tuning.
- **Transformer / LLM-style sequence prediction:** Data-starved. ~14k ticks per match vs billions needed for attention-based models.
- **GA with crossover:** Literature uniformly shows crossover is harmful for ANN weight evolution -- it breaks co-adapted weight configurations. Every modern neuroevolution paper (Uber Deep GA, NRA, OpenAI ES) drops it.
- **CMA-ES:** Ideal at d=750 weights (self-adapts sigma and covariance). More complex to implement; upgrade path from simple GA when needed.
## Decision
**Architecture:**
- Evo_Bot (1v1) with modular gun interface: `feed(state)` / `aim() -> (angle, power)`
- Gun owns its evolution thread (parallel, never blocks inference)
**TOPO_Gun (first gun implementation):**
- Network: 91->8->1 (hidden size configurable), 745 weights
- Input: 30 ticks x (lateral_vel, delta_heading, wall_distance_ahead) + current distance = 91
- Output: guess factor (-1 to +1)
- Bullet power: deterministic distance-based formula (not learned)
- Evolution: population 300, clone loaded champion + small Gaussian mutations (ALL weights, sigma=0.005-0.01), NO crossover, single elite preserved unchanged
- Fitness: virtual bullet hits on 100 randomly sampled replay tape ticks, using real distance-based power
- Replay tape: rolling window ~2000 ticks (configurable)
- Weight persistence: per-opponent -> global fallback -> random init (load order)
- Cold start: first-ever run, don't fire until champion emerges; subsequent runs load weights, fire from tick 1
- Push new champion weights to inference when it hits better than current
**Deferred:**
- NEAT_Gun: deferred until TOPO_Gun hits its ceiling
- Virtual Guns: run multiple guns in parallel, fire whichever has best virtual hit rate
**Boundaries:**
- Bot controls firing discipline (when to shoot, energy management); gun always returns best aim
## Consequences
- GA+ANN finds nonlinear patterns histograms miss, but needs more data (~50+ ticks vs ~15 for guess-factor histogram) before it outperforms
- Fixed topology before NEAT reduces implementation risk
- Mutation-only evolution simplifies implementation (no crossover logic)
- Parallel evolution thread reuses the pattern from SAC_LSTM_Bot (#48)
- Per-opponent weight persistence eliminates cold start after first encounter
- CMA-ES is the natural upgrade path if simple GA convergence is too slow (d=750 is CMA-ES sweet spot)
prototypes/ga_gun_spike/ga_spike.nim
+111
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@@ -0,0 +1,111 @@
## ga_spike.nim — throwaway GA neuroevolution spike: evolve ANN to predict sin(x)
## Validates: population init, fitness eval, truncation selection, gaussian
## mutation on all weights, single-elite preservation, champion tracking.
## Issue #66.
import std/[math, random, algorithm, strformat]
const
InputDim = 10 # sliding window of 10 past sin values
HiddenDim = 4
OutputDim = 1
NumWeights = (InputDim * HiddenDim + HiddenDim) + # W1 + b1
(HiddenDim * OutputDim + OutputDim) # W2 + b2 = 49
PopSize = 200
Generations = 200
TopFrac = 0.2 # keep top 20%
Sigma = 0.01 # gaussian mutation sigma on ALL weights
EvalPoints = 50 # fitness eval sample size
type Individual = object
weights: seq[float64]
fitness: float64
proc forward(w: seq[float64]; input: array[InputDim, float64]): float64 =
## 10->4->1 tanh ANN, flat weight layout: W1[40], b1[4], W2[4], b2[1]
var hidden: array[HiddenDim, float64]
for h in 0..<HiddenDim:
var s = w[InputDim * HiddenDim + h] # b1[h]
for i in 0..<InputDim:
s += w[h * InputDim + i] * input[i] # W1[h,i]
hidden[h] = tanh(s)
var s = w[InputDim * HiddenDim + HiddenDim + HiddenDim] # b2[0]
for h in 0..<HiddenDim:
s += w[InputDim * HiddenDim + HiddenDim + h] * hidden[h] # W2[h]
result = tanh(s)
proc evaluate(ind: var Individual; rng: var Rand) =
## Fitness = -MSE on EvalPoints random samples of sin prediction.
var mse = 0.0
for _ in 0..<EvalPoints:
let x = rng.rand(0.0 .. 20.0 * PI)
var input: array[InputDim, float64]
for i in 0..<InputDim:
input[i] = sin(x - float64(InputDim - 1 - i))
let target = sin(x)
let pred = forward(ind.weights, input)
mse += (pred - target) * (pred - target)
ind.fitness = -mse / EvalPoints.float64
proc mutate(ind: var Individual; rng: var Rand) =
## Additive gaussian on ALL weights, per GA-parameters research.
for i in 0..<ind.weights.len:
ind.weights[i] += rng.gauss(0.0, Sigma)
when isMainModule:
var rng = initRand(42)
# Init population: random weights in [-0.5, 0.5]
var pop = newSeq[Individual](PopSize)
for i in 0..<PopSize:
pop[i].weights = newSeq[float64](NumWeights)
for j in 0..<NumWeights:
pop[i].weights[j] = rng.rand(-0.5 .. 0.5)
echo &"GA spike: {PopSize} individuals, {NumWeights} weights, {Generations} gens, sigma={Sigma}"
for gen in 0..<Generations:
# Evaluate
for i in 0..<PopSize:
pop[i].evaluate(rng)
# Sort descending by fitness (higher = better)
pop.sort(proc(a, b: Individual): int = cmp(b.fitness, a.fitness))
if gen mod 20 == 0 or gen == Generations - 1:
echo &" gen {gen:3d} best_fitness={pop[0].fitness:.6f} (MSE={-pop[0].fitness:.6f})"
# Selection: keep top 20%
let nParents = max(1, int(PopSize.float64 * TopFrac))
# Next generation: elite(1) + mutated children from parents
var next = newSeq[Individual](PopSize)
next[0] = pop[0] # single elite, unchanged
for i in 1..<PopSize:
let parent = rng.rand(0..<nParents)
next[i].weights = pop[parent].weights # clone parent
next[i].mutate(rng)
pop = next
# Final evaluation of champion
pop[0].evaluate(rng)
echo &"\nChampion fitness: {pop[0].fitness:.6f} (MSE={-pop[0].fitness:.6f})"
# Print predictions vs actual
echo "\nPredictions (sample):"
var totalErr = 0.0
let nSamples = 20
for i in 0..<nSamples:
let x = float64(i) * 1.0
var input: array[InputDim, float64]
for j in 0..<InputDim:
input[j] = sin(x - float64(InputDim - 1 - j))
let target = sin(x)
let pred = forward(pop[0].weights, input)
totalErr += abs(pred - target)
echo &" x={x:5.1f} sin(x)={target:+.4f} pred={pred:+.4f} err={abs(pred-target):.4f}"
let avgErr = totalErr / nSamples.float64
echo &"\nAverage absolute error: {avgErr:.4f}"
assert avgErr < 0.1, &"Champion avg error {avgErr:.4f} >= 0.1 — evolution did not converge"
echo "PASS: assert avgErr < 0.1"