SirStone/SirRoboGarage
1
0
Fork 0
Code Issues 28 Pull Requests Actions Projects Releases Wiki Activity

chore: standardize internal bot dir structure (src/, tests/, out/)

Browse Source 
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Davide Cappellini
2026-08-27 18:42:03 +02:00
parent c5115fdf61
commit 259e67d7ff
42 changed files with 33 additions and 26 deletions
Download Patch File Download Diff File Expand all files Collapse all files
PPO_Bot_garage/src/PPO_Bot/training.nim
+449
View File
@@ -0,0 +1,449 @@
## training.nim — Trajectory buffer, GAE, and PPO training loop.
## Uses manual backprop through the 3-layer tanh MLP + manual Adam.
## No external autograd dependencies — pure Arraymancer Tensor math.
import arraymancer
import std/[math, random, sequtils]
import ./network
# ── Types ─────────────────────────────────────────────────────────────────────
# ponytail: transitions hold PLAIN fixed-size arrays, never Arraymancer tensors.
# Tensors crossing the bot-thread → main-thread boundary get freed on the wrong
# thread's heap under ORC (bot thread SIGSEGVs mid-round in addEvent — 5 matching
# coredumps). Plain arrays are value types: no heap, no GC, safe to move across
# threads. Tensors are rebuilt from the arrays on the consuming (training) thread.
const
MAX_TRANSITIONS* = 8192 # 10 rounds × ~300 ticks + headroom
type
Transition* = object
state*: array[STATE_DIM, float32] # plain copy, rebuilt as tensor in ppoUpdate
action*: array[ACTION_DIM, float32]
logProb*: float32
reward*: float32
value*: float32 # critic estimate at collection time
done*: bool # true at episode (round) boundary
TrajectoryBuffer* = object
transitions*: array[MAX_TRANSITIONS, Transition]
len*: int
# ── Buffer ─────────────────────────────────────────────────────────────────────
proc initTrajectoryBuffer*(): TrajectoryBuffer =
result = TrajectoryBuffer()
proc add*(buf: var TrajectoryBuffer, t: Transition) =
## ponytail: fixed 8192 cap — 10 rounds × ~300 ticks with headroom. Drops
## new transitions when full. Raise cap if accumulation window grows.
if buf.len < MAX_TRANSITIONS:
buf.transitions[buf.len] = t
inc buf.len
proc clear*(buf: var TrajectoryBuffer) =
buf.len = 0
# ── Tensor → plain array (same-thread use; tensors never cross threads) ─────
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]
# ── Reward helpers ─────────────────────────────────────────────────────────────
proc computeTickReward*(myEnergyDelta, enemyEnergyDelta: float32;
distToEnemy: float32 = 0.0'f32;
maxDist: float32 = 1.0'f32;
gunBearingAbs: float32 = 180.0'f32): float32 =
## Positive when we deal more damage than we receive.
## Dense shaping: closeness (0-0.01/tick) + aim quality (0-0.02/tick).
## ponytail: magnitudes 10x smaller than original to keep shaping as a nudge,
## not the dominant signal. Increase if bot ignores positioning entirely.
let sparseReward = myEnergyDelta - enemyEnergyDelta
let distReward = 0.01'f32 * (1.0'f32 - distToEnemy / maxDist)
let aimReward = 0.02'f32 * (1.0'f32 - gunBearingAbs / 180.0'f32)
result = sparseReward + distReward + aimReward
proc computeRoundReward*(roundScore: float32): float32 =
## Normalise round-end score to a rough ±6 range (doubled win signal).
## ponytail: cap cumulative score at 400 before /50 — bounded terminal bonus
## keeps critic value scale stable across battle boundaries and long battles.
result = min(roundScore, 400.0'f32) / 50.0'f32
# ── GAE ───────────────────────────────────────────────────────────────────────
proc computeGAE*(rewards, values: seq[float32];
dones: seq[bool];
lastValue: float32;
gamma: float32 = 0.99'f32;
lam: float32 = 0.95'f32):
tuple[advantages: seq[float32], returns: seq[float32]] =
## Generalised Advantage Estimation — reverse sweep with episode boundaries.
## When done=true on transition t, bootstrap value and accumulated GAE are
## reset to 0 at that boundary (terminal state has no future value).
let n = rewards.len
var advantages = newSeq[float32](n)
var lastGae = 0.0'f32
for t in countdown(n - 1, 0):
let nextVal: float32 =
if t == n - 1 or dones[t]: 0.0'f32
else: values[t + 1]
if t == n - 1 or dones[t]:
lastGae = 0.0'f32
let delta = rewards[t] + gamma * nextVal - values[t]
lastGae = delta + gamma * lam * lastGae
advantages[t] = lastGae
var returns = newSeq[float32](n)
for t in 0..<n:
returns[t] = advantages[t] + values[t]
result = (advantages: advantages, returns: returns)
# ── Manual Adam state ─────────────────────────────────────────────────────────
type
AdamState* = object
m*, v*: Tensor[float32]
t*: int
proc initAdamState(like: Tensor[float32]): AdamState =
result.m = zeros_like(like)
result.v = zeros_like(like)
result.t = 0
proc adamStep(param: var Tensor[float32];
grad: Tensor[float32];
state: var AdamState;
lr: float32 = 3e-4'f32;
beta1: float32 = 0.9'f32;
beta2: float32 = 0.999'f32;
eps: float32 = 1e-8'f32) =
inc state.t
state.m = beta1 *. state.m + (1.0'f32 - beta1) *. grad
state.v = beta2 *. state.v + (1.0'f32 - beta2) *. (grad *. grad)
let mHat = state.m /. (1.0'f32 - beta1 ^ state.t.float32)
let vHat = state.v /. (1.0'f32 - beta2 ^ state.t.float32)
param -= lr *. mHat /. (vHat.map(proc(x: float32): float32 = sqrt(x) + eps))
# ── MLP forward with cached activations (for backprop) ────────────────────────
type MLPFwd = object
h1, h2, y: Tensor[float32] # activations (h1=layer1, h2=layer2, y=output)
proc mlpForwardCached(mlp: MLP; x: Tensor[float32]): MLPFwd =
## Forward pass saving intermediate activations needed for backprop.
result.h1 = tanh(mlp.w1 * x + mlp.b1)
result.h2 = tanh(mlp.w2 * result.h1 + mlp.b2)
result.y = mlp.w3 * result.h2 + mlp.b3
proc mlpBackward(mlp: MLP; fwd: MLPFwd; x: Tensor[float32];
gradOut: Tensor[float32]):
tuple[dw1, db1, dw2, db2, dw3, db3: Tensor[float32]] =
## Chain-rule through 3-layer tanh MLP.
## gradOut: [outputDim] — d_loss / d_out
# Layer 3
let dw3 = gradOut.unsqueeze(1) * fwd.h2.unsqueeze(0) # [out, hidden]
let db3 = gradOut
let dh2 = mlp.w3.transpose * gradOut # [hidden]
# tanh backward: d/dx tanh(x) = 1 - tanh²(x)
let dpre2 = dh2 *. (ones[float32](fwd.h2.shape) - fwd.h2 *. fwd.h2)
# Layer 2
let dw2 = dpre2.unsqueeze(1) * fwd.h1.unsqueeze(0) # [hidden, hidden]
let db2 = dpre2
let dh1 = mlp.w2.transpose * dpre2 # [hidden]
let dpre1 = dh1 *. (ones[float32](fwd.h1.shape) - fwd.h1 *. fwd.h1)
# Layer 1
let dw1 = dpre1.unsqueeze(1) * x.unsqueeze(0) # [hidden, input]
let db1 = dpre1
result = (dw1: dw1, db1: db1, dw2: dw2, db2: db2, dw3: dw3, db3: db3)
# ── Adam states for ActorCritic parameters ───────────────────────────────────
type ACAdamStates* = object
## One AdamState per learnable tensor in ActorCritic.
aw1*, ab1*, aw2*, ab2*, aw3*, ab3*: AdamState # actor MLP
cw1*, cb1*, cw2*, cb2*, cw3*, cb3*: AdamState # critic MLP
logStd*: AdamState
initialized*: bool
proc initACAdamStates*(ac: ActorCritic): ACAdamStates =
result.aw1 = initAdamState(ac.actor.w1)
result.ab1 = initAdamState(ac.actor.b1)
result.aw2 = initAdamState(ac.actor.w2)
result.ab2 = initAdamState(ac.actor.b2)
result.aw3 = initAdamState(ac.actor.w3)
result.ab3 = initAdamState(ac.actor.b3)
result.cw1 = initAdamState(ac.critic.w1)
result.cb1 = initAdamState(ac.critic.b1)
result.cw2 = initAdamState(ac.critic.w2)
result.cb2 = initAdamState(ac.critic.b2)
result.cw3 = initAdamState(ac.critic.w3)
result.cb3 = initAdamState(ac.critic.b3)
result.logStd = initAdamState(ac.logStd)
result.initialized = true
# ── Training metrics ──────────────────────────────────────────────────────────
type PPOMetrics* = object
actorLoss*: float32
valueLoss*: float32
gradNorm*: float32
# ── Gradient clipping ─────────────────────────────────────────────────────────
proc globalNorm(grads: varargs[Tensor[float32]]): float32 =
var sumSq = 0.0'f32
for g in grads:
for v in g: sumSq += v * v
result = sqrt(sumSq)
# ── PPO update ────────────────────────────────────────────────────────────────
proc ppoUpdate*(ac: var ActorCritic;
buffer: TrajectoryBuffer;
lastValue: float32;
adamStates: var ACAdamStates;
epochs: int = 4;
miniBatchSize: int = 64;
clipEpsilon: float32 = 0.2'f32;
entropyCoeff: float32 = 0.01'f32;
valueLossCoeff: float32 = 0.5'f32;
lr: float32 = 3e-4'f32;
maxGradNorm: float32 = 0.5'f32;
gamma: float32 = 0.99'f32;
lam: float32 = 0.95'f32): PPOMetrics {.gcsafe.} =
if buffer.len == 0: return
var totalActorLoss = 0.0'f32
var totalValueLoss = 0.0'f32
var totalGradNorm = 0.0'f32
var totalMiniBatches = 0
# Initialise Adam states once; caller persists them across rounds.
# Also reinit if aw1.m has wrong shape (e.g. loaded from old checkpoint with
# different STATE_DIM, leaving a (0,) placeholder after shape-mismatch skip).
if not adamStates.initialized or
adamStates.aw1.m.shape.len == 0 or
adamStates.aw1.m.shape != ac.actor.w1.shape:
adamStates = initACAdamStates(ac)
# 1. GAE
let rewards = buffer.transitions[0 ..< buffer.len].mapIt(it.reward)
let values = buffer.transitions[0 ..< buffer.len].mapIt(it.value)
let dones = buffer.transitions[0 ..< buffer.len].mapIt(it.done)
let (advantages, returns) = computeGAE(rewards, values, dones, lastValue, gamma = gamma, lam = lam)
# 2. Normalise advantages
let n = advantages.len.float32
var advMean = 0.0'f32
for a in advantages: advMean += a
advMean /= n
var advVar = 0.0'f32
for a in advantages: advVar += (a - advMean) * (a - advMean)
advVar /= n
# ponytail: float32 adv noise ~1e-12; advVar < 1e-8 = constant-reward
# (passive) round — dividing by that amplifies noise ~1e4+ and drifts the
# policy into exp() overflow. Center-only, skip the divide.
var normAdv: seq[float32]
if advantages.allIt(it == it and abs(it) < 1e30'f32):
if advVar < 1e-8'f32:
normAdv = advantages.mapIt(it - advMean)
else:
let advStd = sqrt(advVar + 1e-8'f32)
normAdv = advantages.mapIt((it - advMean) / advStd)
else:
normAdv = newSeq[float32](advantages.len) # poisoned input → zero advantages, no-op update
let bufLen = buffer.len
# ponytail: minibatch size <= 0 would make mbEnd == mbStart forever and spin.
# Treat as full-batch; breaks the loop unconditionally.
let mbSizeCap = if miniBatchSize > 0: miniBatchSize else: bufLen
for epochNum in 1..epochs:
# Shuffle indices
var indices = toSeq(0..<bufLen)
shuffle(indices)
var mbStart = 0
while mbStart < bufLen:
let mbEnd = min(mbStart + mbSizeCap, bufLen)
if mbEnd <= mbStart: break
let mbSize = mbEnd - mbStart
# Accumulators for gradients (zero-init)
var dActorW1 = zeros[float32](ac.actor.w1.shape)
var dActorB1 = zeros[float32](ac.actor.b1.shape)
var dActorW2 = zeros[float32](ac.actor.w2.shape)
var dActorB2 = zeros[float32](ac.actor.b2.shape)
var dActorW3 = zeros[float32](ac.actor.w3.shape)
var dActorB3 = zeros[float32](ac.actor.b3.shape)
var dLogStd = zeros[float32](ac.logStd.shape)
var dCriticW1 = zeros[float32](ac.critic.w1.shape)
var dCriticB1 = zeros[float32](ac.critic.b1.shape)
var dCriticW2 = zeros[float32](ac.critic.w2.shape)
var dCriticB2 = zeros[float32](ac.critic.b2.shape)
var dCriticW3 = zeros[float32](ac.critic.w3.shape)
var dCriticB3 = zeros[float32](ac.critic.b3.shape)
for j in mbStart..<mbEnd:
let idx = indices[j]
let tr = buffer.transitions[idx]
let adv = normAdv[idx]
let ret = returns[idx].float32
# Rebuild the state tensor on this (training) thread — transitions hold
# plain arrays so no tensor ever crosses a thread boundary.
let x = tr.state.toTensor()
# ── Actor forward ──
let actorFwd = mlpForwardCached(ac.actor, x)
# Critic forward
let newMean = actorFwd.y # [ACTION_DIM]
# Same clamp as collection (network.nim actorForward): train-time std must
# exactly match the std the acting policy used, or ratios are distorted.
let logStdClamped = ac.logStd.map(proc(v: float32): float32 = clamp(v, logStdFloor, logStdCeiling))
let std = logStdClamped.map(proc(v: float32): float32 = exp(v))
# New log prob
var newLogP = 0.0'f32
for i in 0..<ACTION_DIM:
let mu = newMean[i]
let s = std[i]
let diff = (tr.action[i] - mu) / s
newLogP += -0.5'f32 * diff * diff - ln(s) - 0.5'f32 * ln(2.0'f32 * PI.float32)
# ponytail: float32 exp overflows at ±88; ±20 is deep in clipped-ratio
# territory, so loss/grad are identical to the true ratio
let ratio = exp(clamp(newLogP - tr.logProb, -20.0'f32, 20.0'f32))
# Clipped surrogate
let ratioClipped = clamp(ratio, 1.0'f32 - clipEpsilon, 1.0'f32 + clipEpsilon)
let surr1 = ratio * adv
let surr2 = ratioClipped * adv
# Actor loss per sample = -min(surr1, surr2)
totalActorLoss += -min(surr1, surr2)
# Which branch is active?
let useClipped = (surr2 < surr1)
let dLoss_dSurr = -1.0'f32 / mbSize.float32 # d(-mean(min))/d(min) = -1/N
# d(actor_loss)/d(ratio): only the non-clipped branch passes gradient
let dLoss_dRatio = if useClipped: 0.0'f32 else: dLoss_dSurr * adv
# d(ratio)/d(newLogP) = ratio
let dLoss_dNewLogP = dLoss_dRatio * ratio
# d(newLogP)/d(mean[i]) = (action[i] - mean[i]) / std[i]^2
var dLogP_dMean = newTensor[float32](ACTION_DIM)
for i in 0..<ACTION_DIM:
let s = std[i]
dLogP_dMean[i] = (tr.action[i] - newMean[i]) / (s * s)
# Entropy gradient for logStd:
# entropy = sum_i [ logStd_i + 0.5*(1+ln(2π)) ]
# d(entropy)/d(logStd_i) = 1 (for clamped logStd_i > -3, else 0)
# total loss gradient w.r.t. logStd: -entropyCoeff * d(entropy)/d(logStd)
# Also: d(newLogP)/d(logStd_i) when logStd_i > -3:
# = (action_i - mean_i)^2/std_i^2 - 1
for i in 0..<ACTION_DIM:
let isFloorClamped = (ac.logStd[i] <= logStdFloor)
let isCeilingClamped = (ac.logStd[i] >= logStdCeiling)
if not isFloorClamped:
let s = std[i]
let diff = (tr.action[i] - newMean[i]) / s
let dLogP_dLogStdI = diff * diff - 1.0'f32
let ppoGrad = dLoss_dNewLogP * dLogP_dLogStdI
# Entropy term pushes logStd up (update = param - lr*grad, grad is -entropyCoeff < 0).
# Gate it off at the ceiling to prevent runaway logStd.
let entropyGrad = if isCeilingClamped: 0.0'f32
else: -entropyCoeff / mbSize.float32
dLogStd[i] += ppoGrad + entropyGrad
# Backprop actor gradients
let gradActorOut = dLoss_dNewLogP *. dLogP_dMean # [5]
let actorGrads = mlpBackward(ac.actor, actorFwd, x, gradActorOut)
dActorW1 += actorGrads.dw1
dActorB1 += actorGrads.db1
dActorW2 += actorGrads.dw2
dActorB2 += actorGrads.db2
dActorW3 += actorGrads.dw3
dActorB3 += actorGrads.db3
# ── Critic forward + loss ──
let criticFwd = mlpForwardCached(ac.critic, x)
let newVal = criticFwd.y[0]
# Value loss = (newVal - ret)^2; d/d(newVal) = 2*(newVal-ret)
totalValueLoss += (newVal - ret) * (newVal - ret)
let dVLoss_dVal = valueLossCoeff * 2.0'f32 * (newVal - ret) / mbSize.float32
let gradCriticOut = [dVLoss_dVal].toTensor() # [1]
let criticGrads = mlpBackward(ac.critic, criticFwd, x, gradCriticOut)
dCriticW1 += criticGrads.dw1
dCriticB1 += criticGrads.db1
dCriticW2 += criticGrads.dw2
dCriticB2 += criticGrads.db2
dCriticW3 += criticGrads.dw3
dCriticB3 += criticGrads.db3
# ── Gradient clipping ──
# Collect all grads into a seq for norm computation
var allGrads: seq[Tensor[float32]] = @[
dActorW1, dActorB1, dActorW2, dActorB2, dActorW3, dActorB3,
dLogStd,
dCriticW1, dCriticB1, dCriticW2, dCriticB2, dCriticW3, dCriticB3
]
let norm = globalNorm(allGrads)
# ponytail: NaN/Inf grad norm means something exploded this minibatch
# (extreme logprob ratios, poisoned initial weights, etc.). Skip the
# Adam update entirely — no-op is safer than writing NaN into weights,
# which corrupts all future inference and hangs the bot.
if norm != norm or norm > 1e15'f32:
mbStart = mbEnd
continue
totalGradNorm += norm
inc totalMiniBatches
if norm > maxGradNorm:
let scale = maxGradNorm / norm
for g in allGrads.mitems: g = g *. scale
# Unpack clipped grads
dActorW1 = allGrads[0]; dActorB1 = allGrads[1]
dActorW2 = allGrads[2]; dActorB2 = allGrads[3]
dActorW3 = allGrads[4]; dActorB3 = allGrads[5]
dLogStd = allGrads[6]
dCriticW1 = allGrads[7]; dCriticB1 = allGrads[8]
dCriticW2 = allGrads[9]; dCriticB2 = allGrads[10]
dCriticW3 = allGrads[11]; dCriticB3 = allGrads[12]
# ── Adam updates ──
adamStep(ac.actor.w1, dActorW1, adamStates.aw1, lr)
adamStep(ac.actor.b1, dActorB1, adamStates.ab1, lr)
adamStep(ac.actor.w2, dActorW2, adamStates.aw2, lr)
adamStep(ac.actor.b2, dActorB2, adamStates.ab2, lr)
adamStep(ac.actor.w3, dActorW3, adamStates.aw3, lr)
adamStep(ac.actor.b3, dActorB3, adamStates.ab3, lr)
adamStep(ac.logStd, dLogStd, adamStates.logStd, lr)
# Collection clamps logStd to [floor, ceiling] at inference; clamp the raw
# param after the step so it can't drift above the ceiling (the old code
# only clamped at collection → train-time recompute used a bigger std than
# the policy that actually acted → distorted importance ratios).
ac.logStd = ac.logStd.map(proc(v: float32): float32 = clamp(v, logStdFloor, logStdCeiling))
adamStep(ac.critic.w1, dCriticW1, adamStates.cw1, lr)
adamStep(ac.critic.b1, dCriticB1, adamStates.cb1, lr)
adamStep(ac.critic.w2, dCriticW2, adamStates.cw2, lr)
adamStep(ac.critic.b2, dCriticB2, adamStates.cb2, lr)
adamStep(ac.critic.w3, dCriticW3, adamStates.cw3, lr)
adamStep(ac.critic.b3, dCriticB3, adamStates.cb3, lr)
mbStart = mbEnd
let totalSamples = (epochs * bufLen).float32
result.actorLoss = totalActorLoss / totalSamples
result.valueLoss = totalValueLoss / totalSamples
result.gradNorm = if totalMiniBatches > 0: totalGradNorm / totalMiniBatches.float32
else: 0.0'f32