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
806 B
YAML
47 lines
806 B
YAML
run:
|
|
experiment_name: ismcts-mini
|
|
max_iterations: 50
|
|
seed: 1
|
|
device: cpu
|
|
rules:
|
|
n_colors: 3
|
|
n_ranks: 5
|
|
min_rank: 2
|
|
n_handshakes: 1
|
|
hand_size: 4
|
|
expedition_penalty: -20
|
|
bonus_threshold: 4
|
|
bonus_amount: 20
|
|
encoding:
|
|
derived_playability: true
|
|
slot_aware_playability: true
|
|
network:
|
|
kind: mlp
|
|
hidden_size: 128
|
|
num_layers: 2
|
|
activation: relu
|
|
mcts:
|
|
n_simulations: 50
|
|
c_puct: 1.5
|
|
max_depth: 100
|
|
temperature:
|
|
training: 1.0
|
|
eval: 0.0
|
|
training:
|
|
games_per_iter: 10
|
|
gradient_steps_per_iter: 10
|
|
batch_size: 128
|
|
replay_capacity: 50000
|
|
optimization:
|
|
learning_rate: 0.001
|
|
grad_clip: 5.0
|
|
checkpoint:
|
|
save_every: 0
|
|
save_latest: true
|
|
evaluation:
|
|
eval_every: 5
|
|
games: 20
|
|
opponents: [random, discard-only, heuristic-cautious]
|
|
max_steps: 300
|
|
num_workers: 1
|