coolguyandClaude Opus 4.7
acb664c873
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>
105 lines
2.1 KiB
YAML
105 lines
2.1 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: 2
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traversals_per_player: 70
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max_depth: null
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max_nodes_per_traversal: 10000
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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: 4
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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: none
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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: 1
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strategy_updates_per_iteration: 1
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advantage_batch_size: 1024
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strategy_batch_size: 1024
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advantage_updates_per_iteration: 512
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strategy_updates_per_iteration: 512
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learning_rate: 0.00003
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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: 5
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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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