Default is one baseline + one treatment, sequential, same seed, with a
shared --wandb-tag hypothesis label for W&B Compare Runs filtering.
Multi-seed only on explicit request; never run two trainings on the same
GPU.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Brief guidance on roles (notes = purpose, tags = filter categories)
plus three anti-patterns to avoid. Otherwise free-form.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Exposes wandb.init's notes field through the CLI so each run can carry a
short description of its purpose, visible on the W&B run page alongside
tags and config.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
- AGENTS.md: add Weights & Biases section covering install (extra),
online/offline modes, per-run wandb/ layout, sync command, and the
source-of-truth note (metrics.jsonl, not W&B).
- CLAUDE.md: replace soft "before making changes" wording with a
mandatory session-start instruction to read AGENTS.md in full.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Append eval_seconds and its share of iteration_seconds to the per-iteration
console summary when evaluation actually ran, so users watching the log can
see how much wall time eval is consuming without parsing JSON metrics.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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>
Mirror Deep CFR training metrics to W&B via a new WandbRunTracker
wired through CompositeRunTracker; wandb is an optional extra so
default installs and runs stay unchanged. Train CLI gains
--wandb/--wandb-project/--wandb-mode/--wandb-name/--wandb-tag, and
train() now closes the tracker in a finally block so runs finalize
even on early exit.
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>
같은 hyperparameter (512x3, 2x updates, argmax_tiebreak)에 opponent_policy
만 self_play_league로 다른 run과의 iteration별 비교 표 추가. iter 350
시점에서 Random WR 38%p 격차 (network 34% vs league 72%) 확인 — 발산
가설을 실증.
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>
- Add network.kind field to config (mlp/color_shared)
- Implement ColorSharedNetwork that:
- Splits input into 5 equal color blocks
- Encodes each block with shared weights
- Pools with mean/max aggregation
- Concatenates pooled embeddings with remainder
- Outputs same action logits as MLP
- Add ColorAttention for optional self-attention over color embeddings
- Add network.color_attention_layers and color_attention_heads config
- Maintain full backward compatibility (default kind=mlp)
- Add 28 comprehensive tests covering all architectures
Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
coolguyandClaude Opus 4.7