computeRoundReward used cumulative totalScore/50 — unbounded in long battles
(vLoss 353 at round 3160 → 25745 by 3871 in the 5841-round attempt). Cap the
score term at 400 before /50: bonus ∈ [0,8], so the critic's value scale stays
stable regardless of battle length and across battle boundaries.
Reverts the 60-round battle chunking (186e005/da2f825): one battle per
campaign for the whole remaining budget; keeps the crash-restart loop, the
mid-battle freeze guard and the end-of-battle counter completeness check.
Cert (5211, single 60-round battle): 59/60 wins (sole loss = cold-start round
1, score 61), vLoss avg 36.1 / max 148.5, gNorm max 596, zero NaN, zero
restarts, counter check passed. Weights persist to round 5211.
The event queue's heap seq was the last GC'd block surviving across
rounds: each round runs on a freshly spawned bot thread, so the N+1
thread realloc'd a block grown by dead thread N's allocator mid-round
(at the next capacity doubling, ~turn 104) -> rawDealloc SIGSEGV in
addEvent (7 gdb-confirmed coredumps). Replace with a static
array[MAX_QUEUE_SIZE, BotEvent] + eventsLen: no heap block crosses
threads, realloc can never happen.
Also fix the harness aborting the final round mid-train: PPO_Bot's
onRoundEnded trains synchronously after the runner's RoundEndedEvent,
so the counter read right after awaitResults() is the stale pre-train
value and System.exit killed the bot inside ppoUpdate. Poll up to 60s
for the counter to catch up before declaring the battle incomplete.
Verified: 72 consecutive rounds vs Fire, 100% wins, all rounds trained
(counter advanced 1:1), zero coredumps since the fix.
Save ACAdamStates (m/v tensors + t counters) as .npy files alongside
network weights in latest/ and checkpoint dirs; save round counter to
round_counter.txt. loadBestAvailable restores both on startup; fresh
start works unchanged when files are absent.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- enemy_tracker: toggle lastOvershootDir each tick; make getRadarTurnRate take var tracker
- training: remove threadvar Adam globals; pass adamStates as var param to ppoUpdate; export ACAdamStates
- PPO_Bot: carry ACAdamStates through TrainingArgs/TrainingResult; drop trainingDone bool and Lock — use resultChan.tryRecv() directly as synchronisation
- weights: sort checkpoint dirs newest-first by mtime instead of hardcoded order
- tests/test_training: pass explicit ACAdamStates to ppoUpdate
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Manual-backprop PPO with Adam: TrajectoryBuffer, computeGAE, ppoUpdate
(4 epochs, minibatch 64, clip 0.2, grad norm 0.5). Reward helpers
computeTickReward/computeRoundReward. Bot wired: tick transitions
collected in run loop, ppoUpdate called on onRoundEnded. Fix: add
arraymancer import to PPO_Bot.nim so Tensor resolves at top level.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
SirStone