Cython implementation in heuristic_cy.pyx achieves ~2.55× speedup on
opponent_act_seconds (200-game eval: 59.20s → 23.24s). Original Python
implementation preserved verbatim in heuristic_py.py as the equivalence
reference. Action-sequence equivalence is verified by
test_safe_heuristic_equivalence.py against seeded game corpora.
Key implementation notes:
- File-local wraparound=True override required for negative discard
indexing; Cython global wraparound=False would segfault.
- annotation_typing=False preserves verbatim Python semantics.
- _CachedState materializes hands/expeditions/discards/deck once per
act() call — this is the dominant performance win.
Further C-array optimization of _card_value_for_me /
_card_value_for_opponent / _color_commitment / _bonus_potential is
deferred. The current 2.55× delivers most of the dense-eval future
benefit; further work is gated on actually adopting denser eval
schedules (eval_every=5, games=1000).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Adopt a 5-namespace scheme so wandb groups related metrics in the
sidebar and capture-group regex (eval/(random|safe_heuristic)/win_rate0
vs eval/(?:random|safe_heuristic)/win_rate0) controls panel splitting:
- loss/{advantage,strategy}
- samples/{advantage,strategy,advantage_player_N}
- memory/{advantage,strategy,advantage_player_N}
- time/{iteration_seconds,traversal_seconds,advantage_train_seconds,
strategy_train_seconds,evaluation_seconds,memory_add_seconds,
checkpoint_seconds,batch_tensor_seconds,nodes_per_second,
advantage_player_N_sample_seconds,strategy_sample_seconds}
- traversal/{nodes,terminals,depth_cutoffs,node_limit_cutoffs,
max_depth_reached,endpoints,avg_endpoint_depth,
endpoint_depth_bucket_*,regret_fallback_*,sampled_actions}
- eval/<opponent>/<metric> (3-level so opponent can be the capture group)
`iteration` keeps no namespace (it's the wandb step axis). Internal
TraversalStats.to_dict() and benchmark.py's standalone result dict
keep their flat names — only the trainer's emitted metrics are
remapped, with the traversal_*→traversal/* translation done at
insertion into runtime_metrics.
analyze.py updated to read the new keys (PlotSpec metrics, color map,
opponent_names parser, _first_existing_eval lookup). Tests updated for
the new eval_metrics dict keys.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Make traversal progress and iteration-complete summary lines easier to
visually scan during long runs by leading with [i=N]. Drop the redundant
"iteration=N" kv from the body to keep lines short.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The avg-strategy 1000iter file (with the recent +traversals/+LR/+LCFR
changes) is the canonical "best-known" config. Renamed it to
default.yaml so users start from a single, obvious entry point and
override one field per ablation via --set. Other 12 configs moved to
configs/archive/ — kept for historical reproduction, not for active use.
- configs/deep_cfr/{default.yaml, smoke.yaml} are the only active configs
- experiment_name shortened to "deep-cfr-default" (was a long mouthful)
- AGENTS.md examples and Project Layout section rewritten around
default.yaml; ablation example shows the override-one-field pattern
- Tests pointed at the archived slot-playability config for the legacy
reproduction assertions
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
If eval_every is positive but max_iterations falls before the next scheduled
eval iteration (including resume cases where current_iteration is already
past the last eval boundary), log a one-time warning at run start so the
user notices the misconfiguration. We deliberately do not force an
end-of-run eval, which would distort time budgets and reproducibility.
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>
- 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