Three coordinated hygiene changes; none target the diagnosed
selection-bias bottleneck. They make the codebase honestly reflect the
pure-self-play stance and reduce dashboard noise.
Bot rename (drop the unhelpful safe_ prefix; suffixes describe behaviour):
- 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 loose/strict parameter constants. Backwards compatibility was dropped
intentionally per user instruction; no aliases. Active configs, docs,
scripts, tests updated. Archive directories (configs/archive,
docs/archive, runs/archive) left intact and may still reference old
names per their read-only policy. The src/.../bots/passive.py module was
renamed to discard_only.py via git mv.
Analyze plot curation (deep_cfr/analyze.py):
- Added analysis_00_core.png as the canonical daily 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 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 opponents allowlist so the new core section can pin
a specific opponent per panel without restructuring plot_section.
Tiered evaluation cadence (EvaluationConfig):
- Added extended_opponents and extended_eval_every (default 0 = disabled).
- opponents_for_iteration(iteration) returns the core list 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 the model's raw
average score). heuristic_cautious is the ceiling and the
archive-comparable benchmark used in the prior diagnostic sections.
Net eval cost reduction: roughly 50% (3 opponents x every 5 iter, plus
6 opponents x every 50 iter, vs the prior 6 x every 5).
Documented in docs/plans/deep-cfr-selectivity.md section 9.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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. default.yaml has
shipped with all_negative_fallback: argmax_tiebreak since 618d5f8 based
on the 20-iter audit + 1000-iter empirical comparison in
docs/archive/deep-cfr-regret-fallback-audit-2026-05-07.md, but the
default scheduler was switched to interleaved in 09bbe7c, after which
the configured fallback mode silently no-op'd.
_regret_matching now takes fallback_mode and concentrates policy mass on
the lowest-index tied action when "argmax_tiebreak". Tiebreak is
deterministic; the Cython recursive traverser randomises ties using its
per-traverser RNG, which the batched policy does not have. Behaviour
matches the spirit of the recursive path (concentrate on best, do not
dilute uniformly).
Plumbed through BatchedPolicy, InterleavedTraversalConfig,
run_interleaved_traversal_batch, trainer.py, workers.py, and the
analyze_first_open_targets.py caller. Two unit tests added.
Also bumps default.yaml outcome_sampling_epsilon 0.2 -> 0.05. The
200-iter sweep in docs/plans/deep-cfr-selectivity.md section 1 showed
0.05 produced the best short-run safe_heuristic_strict score diff
(-40.01 vs -57.87 for 0.20). Recent experiments already used 0.05; the
default now matches actual experimental practice.
Neither change targets the diagnosed selection-bias bottleneck. They
align config intent with scheduler behaviour and make the default config
reproduce known-best knob settings out of the box.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
ColorSharedNetwork previously sliced the input vector into n_colors equal
chunks (input_dim // n_colors). 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 "color-shared" encoder
was therefore sharing weights across semantically unrelated chunks, not
across per-color blocks. The single archived run that exercised this path
(2026-05-07_092137_color_shared_attention_1000iter) was killed at iter 41
and produced no eval data, so we have no measurement of whether a real
per-color architecture would help.
Adds compute_lost_cities_color_layout(input_dim) which returns explicit
per-color and common index lists for the standard Lost Cities encoding
(n_colors=5, hand_size=8, n_ranks=9). It recognises input_dim values
171, 219, 249, 297 across derived_playability and slot_aware_playability
flag combinations.
Per-color block (39 dims when derived_playability is on): both players'
expedition state for that color, discard top, public-histogram row,
pending-discard one-hot bit, legal-action draw-pile bit, and the
derived_playability per-color block. Slot-aware features are slot-major
and stay in common.
ColorSharedNetwork.forward now indexes per-color blocks via the layout
when input_dim matches a known schema. For other dims (unit tests,
non-Lost Cities use), it falls back to chunked slicing with a UserWarning
- preserves backward compatibility for tests but makes the legacy
behaviour visible.
No fair test of the new architecture was run as part of this commit.
Documented in docs/plans/deep-cfr-selectivity.md section 7.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Audit of the Deep CFR information-state encoding identified two tiers of
non-pure features and removed both:
- Tier 3 (judgment): is_bad_open_candidate, open_risk_score,
is_safe_continuation. Same heuristic family used to label bad_open in
evaluation, embedded as model input.
- Tier 2 (projection): recoverable_score_no_bonus,
recoverable_margin_no_bonus, min_needed_to_break_even,
cards_needed_for_bonus, has_bonus_path. Mechanical but assumption-laden
("commit and play all currently-playable cards"). The no_bonus form is
asymmetric: it amplifies the immediate -20 penalty while truncating the
+20 bonus upside, biasing the model toward the same "don't open" basin
the diagnostics already flagged.
Input dim 365 -> 297. DERIVED_PLAYABILITY_PER_COLOR 19 -> 15;
SLOT_AWARE_PLAYABILITY_PER_SLOT 12 -> 6. Test shape assertions updated.
Also adds selectivity diagnostic infrastructure used to reach this point:
- traversal.outcome_unsampled_first_open_prior_alpha config field with
signed-prior overlay on unsampled first-open advantage targets (A1).
- analyze_first_open_counterfactual.py --post-policy to swap the
policy_player rollout policy and isolate selection bias (D1).
- analyze_first_open_followup.py to inspect post-forced-open behavior
(E2): same-color play vs discard counts, other-open rate, terminal
hand composition.
Findings recorded in docs/plans/deep-cfr-selectivity.md sections 3-6.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
coolguyandClaude Opus 4.7