coolguy
bec59dfc3c
Mini Lost Cities (3색 5랭크) 50 iter / 20 eval games: - score_diff vs random +33, vs heuristic_cautious -5.7 (45% 승률) - play_action_rate 13~31% (Deep CFR의 0~2% 대비 명확) - trap escaped on mini, "long-horizon credit assignment" 가설 지지 Codex commit e69f316으로 ISMCTS 구현 완료. Full game scale-up이 다음 후보 (100 iter ETA 3시간 추정).
1067 lines
48 KiB
Markdown
1067 lines
48 KiB
Markdown
# Deep CFR Selectivity Investigation
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Last updated: 2026-05-10
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## Current conclusion
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The current Deep CFR baseline is not mainly blocked by model size or LCFR
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time-weighting. The strongest signal so far is a selectivity failure: the model
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does not reliably learn to distinguish good first opens from bad first opens.
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The `outcome_sampling_epsilon=0.05` setting produced a short-run improvement
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around 200 iterations, but the improvement did not hold through 500 iterations.
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Weighting ablations did not recover the 200-iteration peak, so further LCFR
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alpha tuning is lower priority than directly inspecting or changing the
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first-open advantage target.
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## Baseline symptoms
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The 512x3 dense-eval baseline showed improving training losses, but the main
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game-quality metrics against `heuristic_cautious` did not improve enough to
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indicate a useful policy.
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Observed pattern:
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- Advantage loss can move in the expected direction while eval quality remains
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poor.
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- Bad first-open behavior stays high.
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- Open quality, measured by score per opened color, stays negative.
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- Current-policy evaluation did not rescue the result, so the issue is not only
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average-policy lag.
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## Closed hypotheses
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### More outcome sampling helps, but only up to a point
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Short 200-iteration ablations with `outcome_unsampled_regret=zero`:
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| setting | safe strict score diff | win rate | bad open rate | score/opened color |
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| --- | ---: | ---: | ---: | ---: |
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| `epsilon=0.20` | -57.87 | 0.07 | 0.901 | -8.40 |
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| `epsilon=0.10` | -58.66 | 0.05 | 0.913 | -8.63 |
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| `epsilon=0.05` | -40.01 | 0.12 | 0.893 | -6.25 |
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| `epsilon=0.02` | -53.55 | - | - | - |
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Conclusion: `epsilon=0.05` was the best short-run candidate. Lowering to
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`0.02` was worse, and larger values were also worse.
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### regret_matching_epsilon = 1e-4 was the zero-pit countermeasure (prior repo)
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이전 레포(`../coolrl`)에서 zero-pit 발생 당시 시도한 epsilon 튜닝 기록
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(commit `5a98855`, `da0d4cd`, 2026-05-06). 이건 `outcome_sampling_epsilon`이
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아니라 `regret_matching_epsilon`이라는 별도 파라미터.
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- **eps1e3** (`regret_matching_epsilon=1.0e-3`): zero-pit timeout은 줄였지만
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safe 계열 avg_diff 개선 X. selectivity floor를 과하게 만들 가능성 의심.
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- **eps1e4** (`regret_matching_epsilon=1.0e-4`): floor 더 낮춰서 219 iter
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수동 중단. random 승률 0.86 / score_diff +32, safe 승률 0.03~0.05 /
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score_diff -57~-74. zero-pit timeout은 거의 안 나오지만 selectivity
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실패는 그대로.
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`1.0e-4`가 그때 best로 채택되어 현재 `default.yaml`의 기본값으로 남아 있음
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(`regret_matching_epsilon: 0.0001`). R0, R1 양쪽 모두 이 설정 사용 중.
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**이 lever는 이미 당겨져 있다.** R1에서 zero-pit이 다시 나타난 것은
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floor가 풀려서가 아니라, opponent=discard_only가 "do nothing" 균형을
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*수학적으로* 안전하게 만든 별개 원인. epsilon 재조정으로 풀리는 구조가 아님.
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### past-self 풀만으로는 trap 못 깸 (prior repo)
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이전 레포(`../coolrl`) `full_depth` 실험 (commit `33e0368`, 2026-05-06).
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설정 (anchor 없음, 순수 past-self pool):
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```yaml
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self_play_league:
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current_weight: 0.5
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recent_weight: 0.3
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older_weight: 0.2
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max_snapshots: 20
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recent_window: 5
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```
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가설: opponent로 본인 최신만 쓰는 self-mirror 평형이 selectivity emergence를
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막는다면, 과거 자아 snapshot을 섞으면 평형이 깨진다.
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결과 (iter 322 종료):
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- safe 상대 opened_colors: **4.94~4.96** (거의 전색 opening)
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- 5-color opening 빈도: 91% → 93% (iter 110→180, 감소 없음)
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- safe avg_diff: eps1e4 baseline 대비 +25점 회복 (recovery skill은 emerge)
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- terminal rate 100%, node cutoff 0% (truncation 정상)
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판정 (commit 메시지 그대로): "recovery는 self-play로 emerge했지만 selectivity는
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현재 league 평형 안에서 emerge하지 않는다."
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함의 — past-self를 섞어도 본인 과거 자아는 같은 trap policy의 시간축 평행이동일
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뿐이라 self-mirror 평형이 사실상 그대로 유지된다. selectivity는 self-play 가족
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내부 다양성으로 풀리지 않는다.
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### self_play_league에 heuristic anchor 0.15 주입은 trap을 깨지 못함 (prior repo)
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이전 레포(`../coolrl`) `anchor_safe015` 실험 (commit `279d726`, `a77464b`,
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2026-05-06).
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설정:
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- self_play_league에 deterministic `safe_heuristic` anchor를 0.15 weight로
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주입. 나머지 0.85는 current/recent/older self snapshot.
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- traversal에서 15%의 opponent role을 휴리스틱이 담당 → self-mirror 평형
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부분 절단.
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가설: "self-mirror over-opening 평형을 안chor가 깰 수 있는가."
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결과 (1219 iter / 4h 풀 런):
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| metric | 값 |
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| --- | ---: |
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| anchor traversal rate | 15.7% (mechanism 정상) |
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| safe 상대 avg_diff | -57.11 |
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| opened_colors | 4.83 |
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| 5-color opening 빈도 | ~86% |
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| random avg_diff | +48.31 (후퇴 없음) |
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판정 (commit 메시지 그대로): "anchor pressure 0.15가 over-opening 평형을
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깨지 못한 것으로 판정."
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함의 — opponent pool에 외부 정책 일부 섞기는 0.15 정도로는 trap을 못 깬다.
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더 큰 비율(0.5+)은 사실상 self-play 가설 폐기와 동일하므로 별개 카테고리로
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취급해야 한다. discard_only를 anchor로 섞는 변형은 더 약한 신호이므로 같은
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weight로는 더 나쁠 가능성이 큼.
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### Negative unsampled regret was not sufficient
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The `epsilon=0.05` plus `outcome_unsampled_regret=negative_node_value` variant
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ended around safe strict score diff `-44.53` at 200 iterations, worse than
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`epsilon=0.05` with `zero`.
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Conclusion: the negative target suppresses opening, but it does not selectively
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preserve good opens.
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### The 200-iteration epsilon=0.05 peak did not hold
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Confirmation run:
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- Run: `runs/2026-05-09_010747_confirm-eps-005-zero-512x3-det-500`
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- W&B group: `eps005-confirmation-512x3-v1`
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- Config delta:
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- `run.deterministic=true`
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- `run.max_iterations=500`
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- `traversal.outcome_sampling_epsilon=0.05`
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- `traversal.outcome_unsampled_regret=zero`
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Safe heuristic strict metrics:
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| iteration | win rate | score diff | bad open rate | score/opened color |
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| ---: | ---: | ---: | ---: | ---: |
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| 200 | 0.12 | -40.01 | 0.893 | -6.25 |
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| 250 | 0.08 | -46.91 | 0.900 | -7.29 |
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| 300 | 0.08 | -45.93 | 0.895 | -6.30 |
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| 350 | 0.07 | -54.02 | 0.899 | -7.83 |
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| 400 | 0.04 | -49.64 | 0.912 | -7.83 |
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| 450 | 0.06 | -49.31 | 0.917 | -6.71 |
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| 500 | 0.06 | -61.24 | 0.903 | -8.30 |
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Conclusion: `epsilon=0.05` creates a real short-run peak, but the behavior is
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not stable through 500 iterations.
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### LCFR time-weighting is not the sole cause of the degradation
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Weighting ablation group:
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- W&B group: `eps005-weighting-ablation-512x3-v1`
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- Common config:
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- `run.deterministic=true`
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- `run.max_iterations=300`
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- `traversal.outcome_sampling_epsilon=0.05`
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- `traversal.outcome_unsampled_regret=zero`
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Compared runs:
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| run | iter 200 diff | iter 250 diff | iter 300 diff | iter 300 win | iter 300 bad open |
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| --- | ---: | ---: | ---: | ---: | ---: |
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| LCFR alpha=1.0 confirmation | -40.01 | -46.91 | -45.93 | 0.08 | 0.895 |
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| `training_weighting.mode=none` | -50.98 | -48.46 | -56.27 | 0.04 | 0.912 |
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| `training_weighting.lcfr_alpha=0.5` | -66.81 | -58.97 | -50.77 | 0.10 | 0.891 |
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Open-quality comparison:
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| run | iter 200 score/opened color | iter 300 score/opened color |
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| --- | ---: | ---: |
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| LCFR alpha=1.0 confirmation | -6.25 | -6.30 |
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| `training_weighting.mode=none` | -7.71 | -7.70 |
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| `training_weighting.lcfr_alpha=0.5` | -9.94 | -7.85 |
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Conclusion: neither removing time-weighting nor softening LCFR to `alpha=0.5`
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beat the original `alpha=1.0` confirmation by the primary score-diff metric at
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300 iterations. LCFR may affect stability, but it is not the main lever.
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## First-open diagnostic
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Diagnostic output:
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- `runs/tmp/first_open_advantage_confirm_eps005_200_vs_500.jsonl`
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Observed values:
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| checkpoint | bad selection | good selection | bad advantage | good advantage | sampled turns |
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| --- | ---: | ---: | ---: | ---: | ---: |
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| iter 200 | 0.0625 | 0.0582 | -12.82 | -16.60 | 8560 |
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| iter 500 | 0.0067 | 0.0067 | -35.42 | -44.32 | 40942 |
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Interpretation: by 500 iterations the model strongly suppresses opening
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overall. It suppresses good opens along with bad opens, which is the core
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selectivity failure.
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## First-open target audit
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Script:
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- `scripts/analyze_first_open_targets.py`
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Output:
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- `runs/tmp/first_open_target_audit_confirm_eps005_200_vs_500.jsonl`
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Method: regenerate short interleaved traversal batches from existing
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checkpoints and bucket first-open advantage targets by action quality. Because
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`outcome_unsampled_regret=zero` sets unsampled legal actions to zero, the most
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informative statistic is the sampled-action target distribution, not the full
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legal-candidate target distribution.
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Sampled target summary:
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| checkpoint | bucket | candidates | policy prob | sampled rate | sampled target mean | sampled target positive |
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| --- | --- | ---: | ---: | ---: | ---: | ---: |
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| iter 200 | good open | 796 | 0.078 | 0.078 | -40.71 | 0.419 |
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| iter 200 | bad open | 7142 | 0.081 | 0.081 | -25.65 | 0.424 |
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| iter 500 | good open | 800 | 0.064 | 0.058 | 8.39 | 0.435 |
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| iter 500 | bad open | 8322 | 0.069 | 0.071 | 12.61 | 0.433 |
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Interpretation: the regenerated traversal targets do not rank good opens above
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bad opens. At both inspected checkpoints, bad-open candidates receive slightly
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higher policy probability and sampled rate than good-open candidates. The
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sampled target mean is also better for bad opens than good opens. This points
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to a target or metric-alignment problem before model capacity or LCFR tuning.
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## First-open counterfactual audit
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Script:
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- `scripts/analyze_first_open_counterfactual.py`
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Output:
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- `runs/tmp/first_open_counterfactual_confirm_eps005_200_vs_500.jsonl`
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Method: collect first-open candidate states from existing checkpoints, force
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each first-open candidate once, and compare the resulting continuation value
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against the current policy's best non-open action from the same state.
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`delta_open = value(force open) - value(best non-open)`.
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Counterfactual summary against `heuristic_cautious`:
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| checkpoint | bucket | candidates | delta mean | delta median | delta positive | policy prob | selected rate |
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| --- | --- | ---: | ---: | ---: | ---: | ---: | ---: |
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| iter 200 | good open | 40 | -26.57 | -26.5 | 0.050 | 0.000 | 0.000 |
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| iter 200 | bad open | 460 | -23.15 | -21.0 | 0.130 | 0.036 | 0.037 |
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| iter 500 | good open | 30 | -23.43 | -17.5 | 0.167 | 0.067 | 0.067 |
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| iter 500 | bad open | 470 | -11.18 | -8.0 | 0.226 | 0.020 | 0.019 |
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Interpretation: the heuristic `open_bad` label is not entirely misaligned with
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continuation value. Forced bad opens are usually worse than the best non-open
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alternative. However, heuristic `open_good` also often loses to best non-open in
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these sampled states, so "recoverable eventually" is not the same as "open now."
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Combined with the target audit, this points toward target/objective alignment:
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the traversal target is not making the bad-open-vs-non-open mistake clearly
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negative, even when the counterfactual continuation usually is negative.
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## Open questions
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1. Does the traversal target itself provide separable labels for good first
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opens versus bad first opens?
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2. Is the model receiving too sparse or too noisy a signal at the first-open
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decision point?
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3. Would target shaping around first-open decisions improve score/opened color
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without increasing bad-open rate?
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4. Is evaluation showing a policy-selection problem, or is the advantage model
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already misranking good and bad opens before strategy extraction?
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## Recommended next experiments
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### 1. Deeper first-open target audit
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Before another long training run, inspect sampled first-open decision records
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more directly:
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- Group candidate first-open actions into good-open and bad-open buckets.
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- Compare target values before model prediction, not only final learned
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advantages.
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- Report distributions, not just means.
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- Check whether the target ranks good opens above bad opens in the same
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information-state context family.
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Success criterion: the target distribution should show a usable separation
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between good and bad opens. If it does not, the training target is the blocker.
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### 2. First-open replay reweighting
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Implemented option:
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- `optimization.advantage_first_open_fraction`
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Meaning: reserve this fraction of each advantage minibatch for samples where a
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first-open action was legally available at the traverser decision. The target is
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not changed; only replay sampling frequency changes. This keeps the experiment
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on the pure self-play side more than hand-written target shaping.
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Initial planned run:
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- `run.experiment_name=first-open-reweight-50-512x3-det-500`
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- `optimization.advantage_first_open_fraction=0.5`
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- `traversal.outcome_sampling_epsilon=0.05`
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- `traversal.outcome_unsampled_regret=zero`
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- `run.deterministic=true`
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- `run.max_iterations=500`
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Primary comparison is the `eps=0.05` confirmation run and the pure external
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sampling run. Success requires bad-open rate and score/opened color to improve
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together without a large score-diff regression.
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Result:
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- Run: `runs/2026-05-09_235808_first-open-reweight-50-512x3-det-500-indexed`
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- W&B group: `first-open-replay-v1`
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- Commit: `8217cec` (`Speed up first-open memory sampling`)
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The scan-based first implementation was stopped after 12 iterations because
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first-open sampling scanned the full replay memory and pushed iteration time
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above 60 seconds. The indexed-memory version kept first-open sampling near
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0.25 seconds per player at 4M advantage samples and completed 500 iterations.
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Final `heuristic_cautious` comparison:
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| Run | Iter | Score diff | Win rate | Bad open | Score/opened |
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| --- | ---: | ---: | ---: | ---: | ---: |
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| baseline `confirm-eps-005-zero-512x3-det-500` | 500 | -61.24 | 0.06 | 0.903 | -8.30 |
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| pure external `pure-external-512x3-det-500` | 500 | -50.38 | 0.06 | 0.925 | -9.79 |
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| first-open reweight 50% indexed | 500 | -52.20 | 0.07 | 0.919 | -9.39 |
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Conclusion: first-open replay reweighting alone did not solve selectivity. It
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improved score diff versus the baseline final checkpoint, but it did not reduce
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bad-open rate and made score/opened color worse. The run briefly looked better
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around 120-200 iterations, then regressed by 300-500 iterations. This suggests
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the replay emphasis is not enough if the underlying target does not separate
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good first opens from bad first opens.
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### 3. First-open target prior (A1) — running 2026-05-10
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Hypothesis: target audit shows the regenerated traversal target does not
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separate good first opens from bad first opens. With
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`outcome_unsampled_regret=zero`, every unsampled first-open candidate is
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labeled with target `0`, regardless of whether it is heuristically good
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or bad. A small signed prior on unsampled first-open play actions should
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break the symmetry without overriding the high-variance sampled-action
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target.
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Implementation:
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- Config field: `traversal.outcome_unsampled_first_open_prior_alpha` (float,
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default `0.0`).
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- Plumbed through `InterleavedTraversalConfig` and
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`run_interleaved_traversal_batch`. Cython traversal path unchanged
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(default scheduler is interleaved; pyx path is a follow-up if needed).
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- Effect site: `_after_child` in `interleaved_traversal.py`. When
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`is_first_open` and `alpha != 0`, unsampled legal play actions whose color
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has an empty expedition are overwritten in `target` with `+alpha` if the
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visible `recoverable_score` for that color is `>= 0`, else `-alpha`. The
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sampled action's cell stays as `sampled_action_value - node_value`.
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- `recoverable_score` mirrors `evaluate._visible_recoverable_summary` for
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the empty-expedition case (no future-card lookahead, hand-only signal).
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- Tests: `test_first_open_prior_overrides_unsampled_play_targets_with_signed_alpha`,
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`test_first_open_prior_zero_alpha_is_noop`.
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|
||
Note on "pure self-play": this prior introduces a hand-written heuristic
|
||
into the advantage target. The opponent and rollout policy remain
|
||
self-play (no external bot). This is treated as a diagnostic experiment;
|
||
if effective, the permanent solution is a counterfactual-based prior
|
||
(A2) that recovers full pure self-play.
|
||
|
||
Initial planned run:
|
||
|
||
- `run.experiment_name=first-open-prior-alpha5-512x3-det-200`
|
||
- `traversal.outcome_unsampled_first_open_prior_alpha=5.0`
|
||
- `traversal.outcome_unsampled_regret=zero`
|
||
- `traversal.outcome_sampling_epsilon=0.05`
|
||
- `run.deterministic=true`
|
||
- `run.max_iterations=200`
|
||
- W&B group: `first-open-prior-v1`
|
||
|
||
Primary comparisons:
|
||
|
||
- `confirm-eps-005-zero-512x3-det-500` (baseline; iter 200 reference)
|
||
- `first-open-reweight-50-512x3-det-500-indexed` (replay-side intervention)
|
||
|
||
Success criterion: `bad_open_rate` and `score_per_opened_color` improve
|
||
*together* at iter 200 without large regression in `avg_score_diff0`. If
|
||
positive, follow up with a 500-iter run to test stability. If null/regress,
|
||
try alpha sweep (e.g. 2.0, 10.0) before abandoning the direction.
|
||
|
||
Result (2026-05-10):
|
||
|
||
- Run: `runs/2026-05-10_072718_first-open-prior-alpha5-512x3-det-200`
|
||
- W&B: synced online to group `first-open-prior-v1` (run `teuh915r`)
|
||
- Commit: see HEAD at run start
|
||
|
||
`heuristic_cautious` at iter 200:
|
||
|
||
| Run | Score diff | Win rate | Bad open | Score/opened |
|
||
| --- | ---: | ---: | ---: | ---: |
|
||
| baseline `confirm-eps-005-zero` (iter 200) | -40.01 | 0.12 | 0.893 | -6.25 |
|
||
| **A1 prior α=5.0 (iter 200)** | **-67.54** | **0.02** | **0.796** | **-8.85** |
|
||
|
||
Other opponents at iter 200: random +32.51 / 0.86, heuristic_noisy -71.52 / 0.07,
|
||
heuristic_balanced -81.81 / 0.02, heuristic_aggressive -81.24 / 0.05.
|
||
|
||
Conclusion: **mixed result, net regression.** The prior did shift behavior
|
||
in the intended direction on one axis — `bad_open_rate` dropped from 0.893
|
||
to 0.796 (~10% absolute reduction). This is the only metric where the
|
||
hypothesis "the prior breaks ranking symmetry" looks supported.
|
||
|
||
But the overall game-quality metrics regressed: score diff worsened
|
||
(-40 → -67), win rate collapsed (0.12 → 0.02), and `score_per_opened_color`
|
||
got worse (-6.25 → -8.85). The model fails the success criterion (which
|
||
required `bad_open_rate` AND `score_per_opened_color` to improve together).
|
||
|
||
Two interpretations:
|
||
|
||
1. **α = 5.0 too strong.** The prior is overpowering the sampled-action
|
||
target rather than acting as a weak symmetry-breaker. The good-open prior
|
||
pushes the model to open *more often*, but those forced opens are bad
|
||
*given the actual game state*, just labeled good by the visible-only
|
||
heuristic. The counterfactual audit already flagged this: heuristic
|
||
`open_good` candidates often lose to best non-open in real continuation.
|
||
2. **Heuristic itself misaligned.** Even the right α won't help if the
|
||
sign assigned to candidates is wrong relative to true game value.
|
||
|
||
Next steps (in order):
|
||
|
||
- (A1.b) α sweep at 200 iter: α ∈ {1.0, 2.0} to test "weaker prior" hypothesis.
|
||
If α=2.0 still regresses score diff while reducing bad_open, the prior shape
|
||
itself is wrong, not just strength.
|
||
- (A2) Counterfactual prior: replace heuristic sign with sign of
|
||
`value(force open) - value(best non-open)` from a small in-traversal
|
||
rollout. Cleaner signal, recovers pure self-play, but more expensive.
|
||
- If both fail to improve score diff, the issue is upstream of unsampled
|
||
regret labeling (likely traversal sample distribution or sampled-action
|
||
target variance), and the next experiment family should target those.
|
||
|
||
### 4. D1 diagnostic: counterfactual with strong post-policy (2026-05-10)
|
||
|
||
Question: are forced-open continuation values low because the openings
|
||
themselves are bad, or because the self-play rollout policy poisons the
|
||
post-action play (selection bias)?
|
||
|
||
Method: re-ran `analyze_first_open_counterfactual.py` on the
|
||
`confirm-eps-005-zero-512x3-det-500` baseline checkpoints (iter 200, iter
|
||
500), but with `--post-policy heuristic_cautious`. The opponent and
|
||
state-collection policy stayed the same; only the policy_player's actions
|
||
*after* the forced first action used the strong fixed bot.
|
||
|
||
Output: `runs/tmp/first_open_counterfactual_d1_strong_post_policy_200_vs_500.jsonl`
|
||
|
||
`delta_open = value(force open) - value(best non-open)` comparison:
|
||
|
||
| iter | bucket | n | self-play post | strong post | shift |
|
||
| ---: | --- | ---: | ---: | ---: | ---: |
|
||
| 200 | open_good | 40 | -26.57 | **-3.35** | +23.22 |
|
||
| 200 | open_bad | 460 | -23.15 | **-5.52** | +17.63 |
|
||
| 500 | open_good | 30 | -23.43 | **-10.90** | +12.53 |
|
||
| 500 | open_bad | 470 | -11.18 | **-8.99** | +2.19 |
|
||
|
||
`delta_positive_rate`:
|
||
|
||
| iter | bucket | self-play post | strong post |
|
||
| ---: | --- | ---: | ---: |
|
||
| 200 | open_good | 0.050 | 0.225 |
|
||
| 200 | open_bad | 0.130 | 0.317 |
|
||
| 500 | open_good | 0.167 | 0.300 |
|
||
| 500 | open_bad | 0.226 | 0.230 |
|
||
|
||
Verdict — two findings, both important:
|
||
|
||
**1. Selection bias is real and significant.** Strong post-policy
|
||
improves forced-open continuation values by 12–23 points on average. The
|
||
self-play policy is meaningfully poisoning rollouts: forced opens look
|
||
much less bad once a competent player handles the followup.
|
||
|
||
**2. Heuristic labels do not separate cleanly even under strong post-policy.**
|
||
At iter 200, `open_good` delta_mean is only ~2 points better than
|
||
`open_bad` (-3.35 vs -5.52). At iter 500, ranking is essentially flat
|
||
(`open_good` -10.9 vs `open_bad` -8.99 — slightly *worse*). Median deltas
|
||
agree. Sample size for `open_good` is small (30–40) so noise contributes,
|
||
but there is no clean signal that the heuristic `recoverable_score`
|
||
classifier matches actual continuation value.
|
||
|
||
Implications:
|
||
|
||
- A1 prior was destined to fail — the heuristic sign is at best weakly
|
||
aligned with continuation value, even with optimistic post-policy.
|
||
- A2 with self-play rollouts would inherit selection bias and likely
|
||
reproduce the same misranking. A2 with a strong post-policy would
|
||
give clean signs but breaks pure self-play.
|
||
- The deeper bottleneck is post-open play quality. Until the trained
|
||
policy plays competently *after* opening, training signals about
|
||
*whether to open* will be biased toward "don't open."
|
||
|
||
Candidate next directions (decision pending):
|
||
|
||
- (E1) Train with `cutoff_rollout_policy=heuristic_balanced` instead of `random`.
|
||
Already a config option; gives leaf nodes stronger value estimates
|
||
during traversal. Trades some pure-self-play purity for a stronger
|
||
bootstrap signal. Cheap to test.
|
||
- (E2) Investigate post-open behavior directly: forced-open + observe
|
||
next-2-3 turns. Diagnoses *why* the model can't follow up (e.g. always
|
||
discards followup cards, switches color, etc.).
|
||
- (E3) Curriculum / staged training that exposes the network to good
|
||
post-open trajectories before forcing it to make first-open decisions.
|
||
- A2 deferred unless we adopt a strong post-policy in rollouts.
|
||
|
||
### 5. E2 diagnostic: post-forced-open behavior (2026-05-10)
|
||
|
||
Question: D1 showed selection bias is real — *what is the model actually
|
||
doing after a forced first-open* that poisons rollouts?
|
||
|
||
Method: forced each first-open candidate at sampled states, then observed
|
||
the policy_player's next 3 decisions (window). Used baseline checkpoints
|
||
(`confirm-eps-005-zero` iter 200, iter 500). Categorized each followup
|
||
decision relative to the forced-open color.
|
||
|
||
Output: `runs/tmp/first_open_followup_baseline_200_vs_500.jsonl`
|
||
Script: `scripts/analyze_first_open_followup.py`
|
||
|
||
Per-window mean counts (3 policy_player decisions = ~1.5 game turns;
|
||
each game turn includes one play/discard plus one draw):
|
||
|
||
| iter | bucket | n | held@force | plays(same) | discards(same) | open_other | other_discard | held@end |
|
||
| ---: | --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
|
||
| 200 | good | 33 | 4.82 | 0.15 | 0.15 | **0.49** | 0.21 | 2.70 |
|
||
| 200 | bad | 367 | 1.97 | 0.03 | 0.12 | 0.10 | 0.75 | 1.08 |
|
||
| 500 | good | 22 | 4.45 | 0.14 | **0.46** | 0.14 | 0.27 | 1.23 |
|
||
| 500 | bad | 378 | 2.56 | 0.01 | 0.23 | 0.05 | 0.71 | 1.29 |
|
||
|
||
Findings:
|
||
|
||
**1. Model under-plays followup cards even when it holds many.** At iter
|
||
200, good-open candidates start with ~4.8 same-color cards in hand. In the
|
||
next 3 decisions, the model plays only 0.15 of them on average (3% of
|
||
its window). 2.7 cards remain in hand at terminal — nearly 3 cards of
|
||
the just-opened color never reach the expedition.
|
||
|
||
**2. Model opens additional new colors after a forced open.** At iter 200
|
||
good bucket, `open_other` = 0.49 in a 3-decision window — the model is
|
||
~3× more likely to open *another* new color than to follow up the one it
|
||
was forced into. This compounds the recoverable-score drag.
|
||
|
||
**3. By iter 500 the model actively dumps the forced color.** Good bucket
|
||
at iter 500: `same-color discards` 0.46 vs `same-color plays` 0.14. The
|
||
model discards followup cards more than 3× as often as it plays them,
|
||
despite holding 4.45 of them at force time. This is the strongest single
|
||
piece of evidence so far that the post-open value head is poisoned.
|
||
|
||
**4. Bad-open bucket shows even sharper avoidance.** Hand has ~2
|
||
same-color cards, plays effectively zero. Almost all play-phase decisions
|
||
are "discard other" (0.71–0.75). The model treats forced opens as bad
|
||
news to liquidate rather than commit to.
|
||
|
||
Interpretation: the trained advantage network *learned* that opens of
|
||
these colors are net-negative, so once forced into one, it hedges by
|
||
opening other colors and dumping followup cards. That hedging is rational
|
||
under the policy's own value estimates but is the exact mechanism that
|
||
keeps forced-open continuation values low and keeps the training target
|
||
labeling opens as bad. Closed loop.
|
||
|
||
This combined with D1 means:
|
||
|
||
- A1/A2 priors operate on first-open *labels*. They cannot fix a model
|
||
that, even after committing to an open, refuses to follow up.
|
||
- Any fix needs to either (a) produce stronger leaf values during training
|
||
so the value head learns post-open play matters, or (b) explicitly
|
||
curriculum or prior-shape the *post-open* decision (not the open
|
||
decision).
|
||
|
||
Updated next-step priorities:
|
||
|
||
- **(E1) `cutoff_rollout_policy=heuristic_balanced` training ablation.**
|
||
Strongest single lever: gives traversal leaves stronger value estimates
|
||
during training, which should ripple back to "open + follow up" signal.
|
||
Pure-self-play purity dented, but only at cutoff leaves.
|
||
- (E1.b) Sanity check: at iter 200 baseline, the same-color discard rate
|
||
is already 0.12 in good bucket — early. So this is not a late-training
|
||
collapse; it's baked in from early iterations. Curriculum-style fix
|
||
would have to start very early.
|
||
- A2 effectively dead unless paired with strong post-policy in rollouts.
|
||
|
||
### 6. Input feature audit and cleanup (2026-05-10)
|
||
|
||
After D1 + E2 confirmed the bottleneck is *training signal* and not *input
|
||
poverty*, audited every feature the model receives. Goal: align the input
|
||
representation with a strict pure-self-play definition by removing any
|
||
feature that embeds a hand-coded judgment or strategy assumption.
|
||
|
||
Three tiers found:
|
||
|
||
- **Tier 1**: pure game state and single-step game rules (hand cards,
|
||
expedition state, discard piles, scores, public histogram, legal-action
|
||
mask, `playable_*`/`dead_*` rule checks, `unknown_remaining_count`,
|
||
etc.). No judgment. Kept.
|
||
- **Tier 2**: projection-based features that compute "if I commit and
|
||
play all currently-playable cards, what is the score?" — mechanical
|
||
but assumption-laden. Includes `recoverable_score_no_bonus`,
|
||
`recoverable_margin_no_bonus`, `min_needed_to_break_even`,
|
||
`cards_needed_for_bonus`, `has_bonus_path`. Removed.
|
||
- **Tier 3**: explicit hand-coded judgments — `is_bad_open_candidate`,
|
||
`open_risk_score`, `is_safe_continuation`. Same heuristic family used
|
||
to label `bad_open` in evaluation. Removed.
|
||
|
||
Notable additional finding: the `no_bonus` projection family is
|
||
asymmetric. It includes wager multipliers but excludes the +20 bonus,
|
||
which systematically *under*-estimates the upside of commitable
|
||
expeditions by roughly 20 × P(complete bonus). This bias points in the
|
||
same direction as the phase 1 trap ("opens look like loss"), so removing
|
||
the projection family is consistent with diagnosing-not-baking-in the
|
||
trap.
|
||
|
||
Implementation:
|
||
|
||
- `encoding.pyx`: `DERIVED_PLAYABILITY_PER_COLOR` 19 → 15;
|
||
`SLOT_AWARE_PLAYABILITY_PER_SLOT` 12 → 6 (across two cleanup passes).
|
||
- Test shape assertions updated.
|
||
- Cython module rebuilt.
|
||
|
||
Input dimension change: 365 → 341 (Tier 3 removal) → **297** (Tier 2
|
||
removal). Net 18.6% reduction.
|
||
|
||
Result is not yet measured. The hypothesis is that with the heuristic
|
||
crutches removed, training behavior is a cleaner measurement of what
|
||
Deep CFR can do in this game from raw input. The model may stay in the
|
||
phase 1 trap longer or fail more visibly, both of which are useful
|
||
information.
|
||
|
||
### 7. ColorSharedNetwork chunked-layout bug (2026-05-10)
|
||
|
||
While re-examining the archived `2026-05-07_092137_color_shared_attention_1000iter`
|
||
run (killed at iter 41), discovered the `ColorSharedNetwork` implementation
|
||
in `networks.py` was not actually color-aware. The forward pass split the
|
||
input vector into `input_dim // n_colors` contiguous slices and ran them
|
||
through a shared encoder. The slice boundaries do not align with the actual
|
||
encoding layout — adjacent slices contain phase flags, hand slots,
|
||
expedition state, scores, etc. mixed together. The "shared color encoder"
|
||
was therefore sharing weights across semantically unrelated chunks, not
|
||
across per-color blocks.
|
||
|
||
This means the prior conclusion that "color_shared / attention archive run
|
||
was inconclusive" was charitable. The architecture being measured was a
|
||
chunked-input network mislabeled `color_shared`, not a real per-color
|
||
shared architecture. We have *no* signal on whether a properly per-color
|
||
architecture would help.
|
||
|
||
Fix landed in this same session:
|
||
|
||
- Added `compute_lost_cities_color_layout(input_dim)` in `networks.py`. For
|
||
the standard Lost Cities schema (n_colors=5, hand_size=8, n_ranks=9), it
|
||
recognises the four valid `input_dim` values (171, 219, 249, 297 across
|
||
derived/slot-aware flag combinations) and returns per-color and common
|
||
index lists derived from the actual encoding layout.
|
||
- Per-color block (39 dims with `derived_playability` on): both players'
|
||
expedition state for that color, discard top metadata, public-histogram
|
||
row, pending-discard one-hot bit, legal-action draw-pile bit, and the
|
||
`derived_playability` per-color block. Slot-aware features stay in
|
||
common because they are slot-major, not color-major.
|
||
- `ColorSharedNetwork.forward` now indexes per-color blocks via the layout
|
||
when `input_dim` matches a known schema. For other input dims (unit
|
||
tests, non-Lost Cities use), it falls back to the old chunked slicing
|
||
with a `UserWarning`, preserving backward compatibility for tests but
|
||
making the legacy behaviour visible.
|
||
- New unit tests cover both branches and the layout helper.
|
||
|
||
This is purely an implementation correctness fix; no fair test of the
|
||
architecture has been run yet. Fair test deferred — the diagnosis from
|
||
sections 4–5 (selection bias, post-open behaviour) suggests that even a
|
||
correct color-aware encoder would not break the closed loop on its own.
|
||
|
||
### 8. Interleaved scheduler honours all_negative_fallback (2026-05-10)
|
||
|
||
The interleaved traversal scheduler's `_regret_matching` was hard-coded to
|
||
spread fallback policy uniformly across legal actions, regardless of the
|
||
configured `regret_matching.all_negative_fallback`. The default config has
|
||
shipped with `all_negative_fallback: argmax_tiebreak` since
|
||
`618d5f8 Promote avg-strategy 1000iter to default.yaml` based on prior
|
||
20-iteration audit + 1000-iteration empirical evidence (see
|
||
`docs/archive/deep-cfr-regret-fallback-audit-2026-05-07.md`), but the
|
||
default scheduler was switched to interleaved in `09bbe7c Make interleaved
|
||
traversal the default`, after which the configured fallback mode silently
|
||
no-op'd in interleaved code paths.
|
||
|
||
Fix:
|
||
|
||
- `_regret_matching(advantages, legal_mask, epsilon, fallback_mode="uniform")`
|
||
in `interleaved_traversal.py` now honours `argmax_tiebreak` by
|
||
concentrating policy mass on the lowest-index tied action (deterministic
|
||
tiebreak; the Cython recursive traverser randomises ties using its
|
||
per-traverser RNG, which the batched policy does not have).
|
||
- `BatchedPolicy` accepts `fallback_mode` and threads it through.
|
||
- `InterleavedTraversalConfig` carries `all_negative_fallback`.
|
||
- `run_interleaved_traversal_batch`, `trainer.py`, `workers.py`, and the
|
||
`analyze_first_open_targets.py` callers pass the field through.
|
||
|
||
Also bumped `traversal.outcome_sampling_epsilon` in `default.yaml` from
|
||
0.2 to 0.05. The 200-iteration sweep (section 1) showed 0.05 produced the
|
||
best short-run heuristic_cautious score diff (-40.01 vs -57.87 for
|
||
0.20). All recent experimental runs already used 0.05; the default now
|
||
matches actual experimental practice.
|
||
|
||
These changes do not target the diagnosed selection-bias bottleneck. They
|
||
align config intent with actual scheduler behaviour and make the default
|
||
config reproduce known-best knob settings out of the box.
|
||
|
||
### 9. Naming, plot curation, and tiered eval cadence (2026-05-10)
|
||
|
||
Hygiene changes — none target the diagnosed selection-bias bottleneck,
|
||
but they make the codebase honestly reflect the pure-self-play stance
|
||
and reduce dashboard noise.
|
||
|
||
Bot family rename (drop the unhelpful `safe_` prefix; suffixes now
|
||
describe behaviour):
|
||
|
||
| Old | New |
|
||
| --- | --- |
|
||
| `safe_heuristic_loose` | `heuristic_aggressive` |
|
||
| `safe_heuristic` | `heuristic_balanced` |
|
||
| `safe_heuristic_strict` | `heuristic_cautious` |
|
||
| `noisy_safe` | `heuristic_noisy` |
|
||
| `passive_discard` | `discard_only` |
|
||
|
||
Class renames in `bots/`: `SafeHeuristicBot` → `HeuristicBot`,
|
||
`SafeHeuristicParams` → `HeuristicParams`, `PassiveDiscardBot` →
|
||
`DiscardOnlyBot`, plus the loose/strict parameter constants. Backwards
|
||
compatibility was dropped intentionally — no aliases. Active configs,
|
||
docs, scripts, tests updated; archive files (read-only by policy)
|
||
left intact and may still reference old names.
|
||
|
||
Analyze plot curation (`deep_cfr/analyze.py`):
|
||
|
||
- New `analysis_00_core.png` dashboard with 10 heuristic-free metrics
|
||
(loss/{advantage, strategy}; vs `heuristic_cautious`:
|
||
`avg_score_diff0`, `win_rate0`, `avg_opened_colors`,
|
||
`positive_expedition_rate`, `bonus_expedition_rate`,
|
||
`score_per_opened_color`, `policy_entropy`; vs `random`: `win_rate0`).
|
||
- Removed `analysis_05_open_quality.png` (bad/weak/good open rates,
|
||
recoverable score) and `analysis_07_calibration.png` (calibration gap,
|
||
recoverable mean) — both relied on the heuristic `recoverable_score`
|
||
classifier we already dropped from inputs.
|
||
- Removed `SELECTIVITY_PLOTS` and `plot_selectivity` (heuristic-laden).
|
||
- `SUMMARY_EVAL_METRICS` no longer includes `bad_open_rate` or
|
||
`calibration_gap`.
|
||
|
||
`PlotSpec` gained an optional `opponents` allowlist so the new core
|
||
section can pin a specific opponent per panel without restructuring the
|
||
existing `plot_section` plumbing.
|
||
|
||
Tiered evaluation cadence (`EvaluationConfig`):
|
||
|
||
- Added `extended_opponents: tuple[str, ...]` and `extended_eval_every:
|
||
int = 0`.
|
||
- Method `opponents_for_iteration(iteration)` returns the core list at
|
||
every `eval_every`, and appends `extended_opponents` (de-duplicated)
|
||
when `iteration` is also a multiple of `extended_eval_every`.
|
||
- `default.yaml` now uses 3 core opponents
|
||
(`random`, `discard_only`, `heuristic_cautious`) every 5 iterations
|
||
and 3 extended opponents (`heuristic_balanced`, `heuristic_aggressive`,
|
||
`heuristic_noisy`) every 50 iterations.
|
||
- `random` is the floor sanity. `discard_only` is the zero-pit
|
||
detector / absolute-score reference (its score is always 0, so
|
||
`eval/discard_only/avg_score_diff0` directly equals our model's
|
||
raw average score). `heuristic_cautious` is the ceiling and the
|
||
archive-comparable benchmark used in sections 1–6.
|
||
|
||
Net effect on ongoing eval cost: ~50% reduction (3 opponents × every
|
||
5 iter, plus 6 opponents × every 50 iter, vs the prior 6 opponents
|
||
× every 5).
|
||
|
||
### 10. Short open-selectivity ablation
|
||
|
||
Run a 200-300 iteration ablation only after the target audit identifies a
|
||
specific change. Candidate changes include:
|
||
|
||
- first-open target shaping,
|
||
- modified unsampled-open penalty,
|
||
- outcome sampling focused on open-relevant branches,
|
||
- or strategy/eval selection that separates current and average policy at the
|
||
open decision.
|
||
|
||
Primary metrics:
|
||
|
||
- `eval/heuristic_cautious/avg_score_diff0`
|
||
- `eval/heuristic_cautious/win_rate0`
|
||
- `eval/heuristic_cautious/bad_open_rate`
|
||
- `eval/heuristic_cautious/score_per_opened_color`
|
||
|
||
Do not promote to 500+ iterations unless bad-open rate and score/opened color
|
||
both improve without degrading score diff.
|
||
|
||
## Operational notes
|
||
|
||
- Keep one GPU training run active at a time.
|
||
- Use `tmux` plus `.compute.lock` for training.
|
||
- Mirror real experiments to W&B.
|
||
- Use one W&B group per hypothesis family.
|
||
- Do not start a long 2000-iteration run until a short diagnostic run shows
|
||
stable selectivity improvement.
|
||
|
||
## §10. R1 — discard_only opponent diagnostic (2026-05-10)
|
||
|
||
### Hypothesis
|
||
|
||
Trap의 본질이 **자기참조 회로(self-similar opponent)**인지 **모델 자체
|
||
credit assignment 실패**인지 분리하기 위해, opponent_policy를
|
||
`discard_only` (항상 카드만 버리는 fixed bot)로 고정. opponent
|
||
dynamics를 거의 0으로 만들고 self-reference를 끊은 환경에서 모델이
|
||
trap을 벗어나는지 본다.
|
||
|
||
### Run
|
||
|
||
- Run dir: `runs/2026-05-10_173912_r1-discard-only-opp-512x3-det-300`
|
||
- W&B group: `r1-discard-only-opp-v1`
|
||
- Iters: 300 (R0 끊긴 시점과 같은 길이로 비교)
|
||
- Single change vs R0: `traversal.opponent_policy: discard_only`
|
||
- All else (capacity 2M, eval games 100, eps=0.05) identical
|
||
|
||
### Result (iter 300, vs R0 clean-baseline iter 300)
|
||
|
||
| metric | R0 (over-open) | R1 (zero-pit) | Δ |
|
||
| --- | ---: | ---: | ---: |
|
||
| score_diff vs cautious | -47.99 | **-69.56** | -21.57 worse |
|
||
| opened_colors vs cautious | 4.93 | **2.54** | -2.39 |
|
||
| play_action_rate vs cautious | 0.02 | **0.00** | -0.02 |
|
||
| pos_expeditions/game cautious | 0.76 | **0.08** | -0.68 |
|
||
| neg_expeditions/game cautious | 4.06 | 2.44 | -1.62 |
|
||
| avg_game_length cautious | 1053 | **5282** | +4229 (timeout) |
|
||
| score_diff vs discard_only | -27.19 | **0.00** | +27.19 |
|
||
| opened_colors vs discard_only | 4.98 | **0.00** | -4.98 |
|
||
| bad_open_rate vs discard_only | 0.91 | **0.00** | -0.91 |
|
||
| score_diff vs random | +49.61 | **-9.30** | -58.91 |
|
||
| opened_colors vs random | 4.97 | 1.45 | -3.52 |
|
||
|
||
### Interpretation: trap shifted, did not break
|
||
|
||
Trap 본질 진단 결과 **"자기참조 회로"는 충분조건이 아니다.**
|
||
|
||
- vs `discard_only` opponent: 모델이 **정확히 0개 색을 열고 0점으로 비김**
|
||
(zero-pit). 즉 closed-loop self-reference 깬 후에도 trap이 다른
|
||
형태(zero-pit)로 재출현.
|
||
- vs `random`: R0에서 +49 (제압)하던 상대에게 R1은 -9 (패배). random은
|
||
카드를 두기라도 하니까 음수 expedition으로 끝나도 양수 게임 일부 만들어서 점수
|
||
내지만, R1 모델은 0점 (전혀 안 둠).
|
||
- vs `heuristic_cautious`: opened_colors 절반(4.93→2.54)으로 줄었지만
|
||
play_action_rate 0% — 연 expedition을 채우지 못함. avg_game_length
|
||
5x 증가 → max_steps timeout 다발. score_diff 22점 더 악화.
|
||
- Action 분포 (vs cautious): **discard 50% / draw_pile 50% / play 0% /
|
||
draw_deck 0%**. 즉 "버리고 상대 버린 거 줍기"의 단일 패턴.
|
||
- Loss 트렌드: advantage loss 1500 → 3500 (꾸준히 상승), strategy loss
|
||
0.7 → 1.4 (꾸준히 상승). 모델이 점점 더 noisy한 target을 못 fit함.
|
||
|
||
### What this proves
|
||
|
||
1. **Trap의 본질은 모델 자체 value function 실패 (credit assignment)**, not
|
||
self-reference. self-reference는 trap 강화 요인이지만 제거해도 다른 형태로
|
||
재출현.
|
||
2. **모델이 일관되게 못하는 일은 "expedition에 카드 두기" 행동**. R0에서
|
||
over-open 무차별 5색 + 채우지 못함. R1에서 아예 안 열되 채우지도 않음.
|
||
양 극단 어디든 play_action_rate ≈ 0%.
|
||
3. **Outcome sampling + 본인 정책 rollout만으로 long-horizon credit chain을
|
||
못 닫는다.** "open이 좋은가?"의 답은 "이후 카드를 잘 두는가"에 종속이고,
|
||
모델이 카드 두기를 못 배우는 한 open 가치는 노이즈로만 추정됨.
|
||
|
||
### Recommended next direction
|
||
|
||
이전에 hygiene 개선(R1 = 4M capacity + 500 eval)을 첫 후보로 두었으나, R1의
|
||
명확한 결과로 hygiene으로는 trap 본질 못 풀 것이 거의 확정. **R2는 trap에 직접
|
||
작용하는 변경이어야 함.**
|
||
|
||
후보 (강도 약→강):
|
||
|
||
1. **Curriculum (작은 게임 → 큰 게임)**: 3색 5랭크 mini Lost Cities로 시작 →
|
||
credit chain 짧아짐 → expedition 사이클이 게임 8턴 안에 닫힘. 모델이
|
||
"카드 두기" 학습 가능성 확보. 풀 게임에 weight transfer.
|
||
2. **Opening / playing 네트워크 분리**: closed loop 회로의 한 회선 물리적
|
||
절단. opening 네트워크는 follow-up이 noise여도 자체 신호로 학습 가능.
|
||
아키텍처 작업 큼.
|
||
3. **Cutoff rollout heuristic + external opponent (조합)**: opening 가치
|
||
추정에 외부 합리적 정책 끼움. pure self-play 가설 명확히 폐기.
|
||
|
||
저자 추천: **R2 = curriculum**. 가장 적은 아키텍처 변경으로 credit chain
|
||
직접 단축. 작은 게임에서 follow-up 학습 성공이 풀 게임 학습의 핵심 prior가
|
||
됨.
|
||
|
||
|
||
## §11. R2 — heuristic_balanced opponent diagnostic (2026-05-10)
|
||
|
||
### Hypothesis
|
||
|
||
R1에서 trap의 본질이 자기참조 회로 아님이 확정됐지만 R1은 discard_only 상대라
|
||
zero-pit이 *수학적으로 정답*이라 진단이 흐릿했다. R2는 양수 점수 만드는
|
||
competent 상대(heuristic_balanced)를 두면 zero-pit 균형이 사라지고 모델이
|
||
"카드 두기" 학습 신호를 처음으로 분명히 받는지 확인.
|
||
|
||
### Run
|
||
|
||
- Run dir: `runs/2026-05-10_211554_r2-heuristic-balanced-opp-512x3-det-300`
|
||
- W&B group: `r2-heuristic-balanced-opp-v1`
|
||
- Config: `traversal.opponent_policy: heuristic_balanced` (기본 self_play_league
|
||
→ 휴리스틱). 다른 모든 변수 R0/R1과 동일.
|
||
- Iters: 167 / 300 종료 (조기 중단). 패턴 명확히 드러나서 끝까지 갈 가치 없음.
|
||
- 부수: interleaved scheduler에 heuristic_balanced opponent 새로 추가
|
||
(commit `7d59398`), recursive 안 쓰고 빠른 path 유지.
|
||
|
||
### Result (last eval iter 165)
|
||
|
||
| metric | R0 | R1 | R2 |
|
||
| --- | ---: | ---: | ---: |
|
||
| vs cautious score_diff | -47.99 | -69.56 | -54.38 |
|
||
| vs cautious opened_colors | 4.93 | 2.54 | 1.11 |
|
||
| **vs cautious play_action_rate** | **0.02** | **0.00** | **0.00** |
|
||
| vs cautious per_game_pos_exp | 0.76 | 0.08 | 0.02 |
|
||
| vs cautious per_game_neg_exp | 4.06 | 2.44 | 1.08 |
|
||
| vs cautious avg_game_length | 1053 | 5282 | 7622 |
|
||
| vs cautious bad_open_rate | 0.92 | 0.96 | 0.98 |
|
||
| vs random score_diff | +49.61 | -9.30 | +5.07 |
|
||
| vs discard_only score_diff | -27.19 | 0.00 | -21.54 |
|
||
| advantage loss (steady) | ~2300 | 1500→3500 | ~200 |
|
||
|
||
### Interpretation: trap shape changes, root cause invariant
|
||
|
||
R2는 R1의 zero-pit과 R0의 over-open 사이의 **변형**:
|
||
- opened_colors 1.11 (R0 5.0보다 작고, R1 0.0보다 큼)
|
||
- bad_open_rate **0.98** (R0/R1보다 더 나쁨) — 여는 거의 모든 색이 bad open
|
||
- positive expeditions **0.02개/게임** — 사실상 보너스 절대 못 받음
|
||
- avg_game_length 7622 — max_steps timeout 폭증, 게임 종료 못 시키는 패턴
|
||
- vs random에서 R0의 +50점 우위가 +5로 무너짐 → random에게도 거의 못 이김
|
||
|
||
R2의 advantage loss 200은 R0의 2200 대비 1/10. **모델이 trap 정책을 더
|
||
정확하게 학습 중**(targets가 deterministic heuristic 상대라 분산 작음).
|
||
정확한 fit이지만 fit하는 게 trap.
|
||
|
||
### What three runs together prove
|
||
|
||
| | R0 (self) | R1 (discard) | R2 (heuristic) |
|
||
| --- | ---: | ---: | ---: |
|
||
| opponent type | self-play | passive fixed | competent fixed |
|
||
| trap shape | over-open | zero-pit | small over-open + zero followup |
|
||
| **play_action_rate** | **~0%** | **~0%** | **~0%** |
|
||
|
||
**세 가지 다른 opponent 분포에서 모델 모두 `play_action_rate ≈ 0%`.** opponent
|
||
와 무관한 핵심 결함. credit assignment / 알고리즘 한계의 직접 증거.
|
||
|
||
### Closed: opponent 변경으로 trap 못 풂
|
||
|
||
이 시점에서 opponent dimension은 닫힘. R0(self), R1(passive), R2(competent)
|
||
세 카테고리 모두 trap. opponent 환경 변경은 trap의 외형만 바꾸고 본질을 못 풂.
|
||
|
||
### Recommended direction: SO-ISMCTS 정공법
|
||
|
||
이전 §10 결론에서 curriculum을 1순위로 두었으나 R2 결과로 우선순위 조정:
|
||
|
||
- credit assignment가 알고리즘 자체의 한계임이 R0/R1/R2 누적 증거로 거의 확정
|
||
- opponent 변형은 닫힌 카테고리
|
||
- 다음 정공법은 **search-based bootstrap (SO-ISMCTS)** — value head로 long-horizon credit chain 단축, PUCT search로 가치 추정 분산 줄임. AlphaZero가 비슷한 구조 풀어본 검증된 메커니즘.
|
||
- Curriculum과 network split은 같은 self-play 가족이지만 search 추가만큼 본질 공격은 아님 → R3는 SO-ISMCTS PoC, curriculum은 백업.
|
||
|
||
설계 문서 작성 + 단계별 구현은 별도 plan으로 (추가 예정).
|
||
|
||
|
||
## §12. R3 — SO-ISMCTS mini Lost Cities PoC (2026-05-10)
|
||
|
||
### Hypothesis
|
||
|
||
R0/R1/R2 누적 진단 결과 trap 본질이 알고리즘 자체의 long-horizon credit
|
||
assignment 실패임이 확정. 다음 정공법으로 SO-ISMCTS (Single-Observer
|
||
Information Set MCTS) + AlphaZero-style policy/value head 도입. value head로
|
||
terminal까지 walk 안 하고 search 부트스트랩으로 가치 추정.
|
||
|
||
이번 실험은 **mini Lost Cities (3색 5랭크, hand 4, bonus 4)** 에서 알고리즘이
|
||
작동하는지 PoC 검증. mini로 시작한 이유: full game은 horizon 길어 학습 비용
|
||
큼, mini에서 알고리즘 작동 확인 후 scale-up.
|
||
|
||
### Design
|
||
|
||
- 변종: SO-ISMCTS (정보집합 노드 트리 + 시뮬마다 새 determinization)
|
||
- Determinization: uniform from unseen cards (Bayesian belief 미도입)
|
||
- Selection: PUCT (c_puct=1.5)
|
||
- Network: AlphaZero식 backbone (MLP 128×2 mini용) + policy head + tanh×100 value head
|
||
- Loss: MSE(value) + KL(policy)
|
||
- Tree: 매 결정마다 fresh (subtree 재사용 없음)
|
||
- Chance: closed-loop within sim, re-determinize across sims
|
||
- Pure self-play (양 플레이어 같은 네트워크)
|
||
- 50 simulations / decision (PoC, 향후 200+)
|
||
|
||
### Implementation
|
||
|
||
신규 모듈: `src/coolrl_lost_cities/games/classic/ismcts/`
|
||
- info_set.py, determinization.py, network.py, mcts.py, replay_buffer.py,
|
||
self_play.py, trainer.py, config.py, cli.py
|
||
- CLI: `lost-cities-ismcts train --config configs/ismcts/{mini,default}.yaml`
|
||
- 단위 테스트 7개 (정보집합 카노니컬, determinization 정합성, 네트워크 forward,
|
||
MCTS smoke, self-play 게임 종료, replay 버퍼)
|
||
- 기존 Deep CFR 코드 무수정 (encoding, evaluate만 재사용)
|
||
|
||
Codex commit: `e69f316`.
|
||
|
||
### Result (mini, 50 iter, 20 eval games per opponent)
|
||
|
||
| 상대 | score_diff | win_rate | play_action_rate |
|
||
| --- | ---: | ---: | ---: |
|
||
| random | **+33.1** | 0.90 | 0.13 |
|
||
| **heuristic_cautious** | **-5.7** | **0.45** | **0.22** |
|
||
| discard_only | -20.8 | 0.00 | 0.31 |
|
||
|
||
비교 (full game Deep CFR R0/R1/R2):
|
||
|
||
| metric | Deep CFR (full game) | SO-ISMCTS (mini game) |
|
||
| --- | ---: | ---: |
|
||
| score_diff vs cautious | -48 ~ -70 | **-5.7** |
|
||
| heuristic_cautious 승률 | 0.06 | **0.45** |
|
||
| play_action_rate vs cautious | 0~2% | **22%** |
|
||
|
||
### Interpretation: trap escaped on mini
|
||
|
||
**Deep CFR이 6주 동안 못 풀던 핵심 결함 (play_action_rate ≈ 0%)이 단번에
|
||
해소.** 모델이 처음으로 expedition에 카드를 의미 있게 두고 있음 (13~31%).
|
||
heuristic_cautious 상대 45% 승률은 거의 호각 (mini라 quality threshold 다르긴
|
||
하지만 명확히 다른 학습 regime).
|
||
|
||
진단 가치: **"trap = long-horizon credit assignment, search 부트스트랩으로 풀
|
||
수 있음"** 가설 강하게 지지됨. mini는 horizon 짧아 (게임당 ~15-25 결정)
|
||
search bootstrap의 효과가 직접 확인 가능했음.
|
||
|
||
### Caveats
|
||
|
||
- Mini game은 credit chain이 짧음. full game (5색 9랭크, ~200 결정/게임)에선
|
||
같은 효과 보인다는 보장 없음.
|
||
- Eval 20게임 small sample. SE 큼.
|
||
- 시뮬 수 50은 mini에는 충분했지만 full은 부족할 수 있음.
|
||
- Strategy fusion / sparse exploration 같은 SO-ISMCTS 자체의 알려진 한계는
|
||
mini에서 노출 안 됨. full에선 노출될 가능성.
|
||
|
||
### Next: scale-up to full game
|
||
|
||
R3a 후보: full Lost Cities (5색 9랭크) 100~200 iter 학습. ETA 3~6시간 추정
|
||
(probe 측정 기반). configs/ismcts/default.yaml 기본 설정으로 시작.
|
||
|
||
성공 기준 (mini와 평행):
|
||
- vs random: score_diff > +30, win_rate > 0.7
|
||
- vs heuristic_cautious: play_action_rate > 10% 이상 (Deep CFR의 ~0%와 분명한 차이)
|
||
- 가능하면 score_diff > -30 (Deep CFR의 -50~-70보다 명확히 좋음)
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