coolguyandClaude Opus 4.7
651175e5bd
Key changes for ISMCTS speed and correctness: - Cython port: HeuristicBot helpers (`heuristic_cy.pyx` + new `.pxd`) and ISMCTS searcher (`mcts.pyx`) now run as cdef. Both share a fast unified-action path through GameState's C interface to avoid Python round-trips on hot rollout/tree-walk paths. - Multi-process self-play and eval: `workers.py`, `eval_worker.py`, `interleaved_self_play.py`, plus trainer wiring with ProcessPoolExecutor + spawn context. Eval inside `evaluate.py` is parallel per opponent. - ISMCTS-specific eval (`evaluate.py`) runs MCTS at decision time so the metric matches deploy mode; `evaluation.eval_with_mcts` flag preserves backwards-compatible policy-only eval when needed. - Trainer logs progress per phase (self-play start/done, eval per opponent), and value loss is now scaled by `value_scale` so policy and value losses sit on comparable magnitudes. - Compact info-set key (`info_set.py`) using packed-struct format and child-key reuse during MCTS descent to cut per-step canonicalization. Tests: 19 ISMCTS suite passing, including parity (Cython-vs-Python sequential, batched-vs-sequential visit counts, push/pop round-trip). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
51 lines
918 B
YAML
51 lines
918 B
YAML
run:
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experiment_name: ismcts-default
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max_iterations: 100
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seed: 1
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device: auto
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rules:
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n_colors: 5
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n_ranks: 9
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min_rank: 2
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n_handshakes: 3
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hand_size: 8
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expedition_penalty: -20
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bonus_threshold: 8
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bonus_amount: 20
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encoding:
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derived_playability: true
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slot_aware_playability: true
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network:
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kind: mlp
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hidden_size: 512
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num_layers: 3
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activation: relu
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mcts:
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n_simulations: 50
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c_puct: 1.5
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max_depth: 200
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parallel_simulations: 8
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virtual_loss_value: 1.0
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temperature:
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training: 1.0
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eval: 0.0
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training:
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games_per_iter: 10
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gradient_steps_per_iter: 10
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batch_size: 128
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replay_capacity: 100000
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interleave_games: 8
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interleave_max_batch: 64
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optimization:
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learning_rate: 0.0003
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grad_clip: 5.0
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checkpoint:
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save_every: 10
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save_latest: true
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evaluation:
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eval_every: 10
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games: 20
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opponents: [random, discard-only, heuristic-cautious]
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max_steps: 10000
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num_workers: 1
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