Drop checkpoint.directory from config — config defines what an experiment
is, not where its outputs go. The CLI now computes the run directory from
run.experiment_name plus a timestamp, defaulting to runs/tmp/ for
throwaway runs and runs/ when --keep is passed.
- Remove CheckpointConfig.directory and DeepCFRConfig.checkpoint_path
- DeepCFRTrainer takes run_dir: Path explicitly
- CLI: add --keep boolean; --resume requires an explicit path (no shortcut)
- Auto path: runs/[tmp/]<YYYY-MM-DD_HHMMSS>_<experiment_name-kebab>/
- Rename 13 configs to kebab-case; strip directory: lines; kebab their
experiment_name values
- Rewrite AGENTS.md training/run sections; document
archive/tmp/<flat> layout, --keep, kebab-case scope
- Update tests for new run_dir flow and dropped --resume shortcut
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Remove legacy aliases, rename max_hours to max_minutes, collapse the
four checkpoint save flags into save_every + save_latest, and change
defaults to safer values (opponent_policy=self_play_league,
device=auto, eval_every=50, max_depth=null). Migrate all archived
yaml configs and tests to the new schema.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Strategy network (학습 중인 average policy)를 traversal opponent로 사용하는
새 옵션 추가. Deep CFR 이론적 수렴이 average strategy에 대한 보장이라는
점에 착안 — opponent_policy=network의 발산 문제를 완화할 수 있는지 실증.
구현:
- config: opponent_policy validator에 average_strategy 추가
- traversal.pyx: opponent_policy_id=3, strategy_network 인자, softmax 기반
policy 도출 (_policy_from_strategy_network)
- workers.py: TraversalWorkerBatch에 strategy_network state_dict 추가
- trainer.py: 직렬/병렬 traversal call에 strategy_network 전달
- 1000-iter 실험 config 추가 (opponent_policy=network와 동일 hyperparam)
Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
opponent_policy=network 설정으로 1000-iter 실험 두 개 (512x3, 1024x4)를
돌린 결과 두 실험 모두 policy collapse가 발생함을 확인. 큰 capacity는
plateau를 늘리지만 발산 자체를 막지 못함. 향후 학습은 self_play_league
기본값을 유지할 것을 권고.
- 실험에 사용한 config 두 개 추가
- 발견 분석 문서 추가 (원인, 비교, 권고)
Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
- Add deep_cfr_color_shared_512x3.yaml: color_shared without attention
- Add deep_cfr_color_shared_attention_512x3.yaml: color_shared with 2-layer attention
- Both use 512 hidden size and 3 MLP layers
- Configs are ready for experimental training runs
Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
coolguyandClaude Opus 4.7