coolguy
e69f3165b6
Implements a proof-of-concept single-observer IS-MCTS trainer with AlphaZero-style policy/value network, determinization, replay, self-play, CLI configs, and focused tests. Mini acceptance run reaches positive random eval while keeping play_action_rate above the Deep CFR trap threshold. Tests: uv run python -m pytest tests/games/classic/ismcts/ -x; uv run python -m pytest tests/games/classic/test_deep_cfr_trainer.py -x; uv run lost-cities-ismcts train --config configs/ismcts/mini.yaml
47 lines
817 B
YAML
47 lines
817 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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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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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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