coolguyandClaude Opus 4.7
bac630b50b
Apply consultant + review recommendations to address training-budget shortfall and underutilized weighting: - traversal.traversals_per_player: 70 → 280 (4× sample touches/iter to reduce regret estimate variance early) - optimization.learning_rate: 3e-5 → 1e-4 (was too low for the 512×1024 updates schedule) - training_weighting.mode: none → lcfr (faster convergence; alpha/beta/ gamma fields are inert with mode=none) - evaluation.eval_every: 5 → 25 (eval was costing more wall-clock than training; 6 opponents × 100 games × 200 evals adds up) - Drop accidental duplicate keys in traversal/optimization sections (YAML last-wins, harmless but confusing) Wall-clock estimate ~13h on the existing setup. If results clearly improve, consider 8× traversals (560) as a follow-up. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
100 lines
2.0 KiB
YAML
100 lines
2.0 KiB
YAML
run:
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experiment_name: lost-cities-deep-cfr-opponent-average-strategy-512x3-1000iter
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seed: 79
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max_iterations: 1000
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max_minutes: null
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device: cuda
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use_amp: false
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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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hidden_size: 512
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num_layers: 3
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activation: relu
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traversal:
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traversals_per_player: 280
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max_depth: null
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max_nodes_per_traversal: 1000
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regret_matching_epsilon: 0.0001
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outcome_sampling_epsilon: 0.2
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outcome_sampling_value_clip: 500.0
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outcome_unsampled_regret: zero
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cutoff_value_mode: score_diff
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cutoff_rollouts: 0
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cutoff_rollout_policy: random
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cutoff_rollout_max_steps: 300
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opponent_policy: average_strategy
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strategy_sample_interval: 1
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store_strategy_on_traverser_nodes: true
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store_strategy_on_opponent_nodes: false
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num_workers: 8
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worker_chunk_size: 8
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progress_every_traversals: 10
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endpoint_depth_bucket_width: 100
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endpoint_depth_bucket_max: 1000
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regret_matching:
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all_negative_fallback: argmax_tiebreak
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training_weighting:
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mode: lcfr
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self_play:
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snapshot_every: 1
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max_snapshots: 0
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anchor_probability: 0.0
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current_weight: 1.0
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recent_weight: 0.0
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older_weight: 0.0
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anchor_weight: 0.0
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recent_window: 5
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optimization:
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advantage_updates_per_iteration: 512
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strategy_updates_per_iteration: 512
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advantage_batch_size: 1024
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strategy_batch_size: 1024
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learning_rate: 1.0e-4
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weight_decay: 0.0001
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grad_clip: 1.0
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memory:
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advantage_capacity: 2000000
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strategy_capacity: 2000000
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checkpoint:
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save_latest: true
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save_every: 100
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progress_interval_seconds: 20.0
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exact_resume: false
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evaluation:
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eval_every: 25
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games: 100
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opponents:
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- random
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- passive_discard
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- safe_heuristic
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- safe_heuristic_loose
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- safe_heuristic_strict
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- noisy_safe
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max_steps: 10000
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on_max_steps: score_diff
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batch_size: 64
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device: trainer
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num_workers: 4
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