sac_train.sh orchestrates chunked self-play via tools/training_runner/
RunTraining.java: weighted opponent sampling per chunk, deterministic
eval (SACLSTM_EVAL_MODE=1) every N chunks with win-rate tracking, best
checkpoint (weights/sac_best.zip) by eval score, crash-restart loop on
the runner's liveness detection.
Supporting changes:
- integration.nim: opponentKey() keys the NewBattle buffer-clear rule on
getBotName(id) with numeric-id fallback (#49 Q14 follow-up);
bumpRoundCounter() emits the per-round liveness signal.
- SAC_LSTM_Bot.nim: onRoundEnded -> bumpRoundCounter().
- RunTraining.java: BOT_NAME env parameterizes result matching
(default PPO_Bot, unchanged behavior for PPO).
- Launch packaging: root SAC_LSTM_Bot.json + .sh for the booter;
src json name aligned to 'SAC_LSTM_Bot' so self-reported identity
matches the booted identity (mismatch = runner connect timeout).
Save/load all SAC-LSTM tensors (actor, 2 critics, 2 target critics,
alpha, Adam states) into a single .zip of .npy files. Atomic write
via temp path + rename. Adam types (AdamVar, SACAdamStates) defined
here for training.nim to use.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
SirStone