332b3eb120
- Added lr_decay_type (0=constant, 1=linear, 2=cosine) and lr_min to the TOML training profile system. Wired through apply_to() with total_steps derived from epochs. - dqn-localdev.toml: lr_decay_type=2 (cosine 1e-4→1e-5 over 200 epochs), min_epochs_before_stopping=80 (was 50, gives model more room to recover after C51 transition). - Smoketest config unchanged (lr_decay_type not set → default Constant). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
74 lines
1.4 KiB
TOML
74 lines
1.4 KiB
TOML
# DQN Local Dev Profile — extended training on RTX 3050 (4GB VRAM)
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# Larger buffer + more episodes than smoketest for meaningful convergence.
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# gpu_n_episodes=64 × gpu_timesteps=200 = 12,800 experiences/epoch
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# buffer_size=50K → fills over ~4 epochs, reducing memorization
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[training]
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epochs = 200
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batch_size = 64
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learning_rate = 0.0001
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gamma = 0.95
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weight_decay = 0.0001
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adam_epsilon = 1e-8
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warmup_steps = 0
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hidden_dim_base = 64
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max_steps_per_epoch = 200
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reward_scale = 1.0
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huber_delta = 1.0
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lr_decay_type = 2
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lr_min = 0.00001
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[distributional]
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num_atoms = 51
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[exploration]
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epsilon_start = 1.0
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epsilon_end = 0.05
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epsilon_decay = 0.85
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noisy_sigma_init = 0.3
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entropy_coefficient = 0.001
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count_bonus_coefficient = 0.1
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q_gap_threshold = 0.05
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[replay_buffer]
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buffer_size = 50000
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min_replay_size = 500
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[early_stopping]
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enabled = true
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patience = 20
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min_epochs_before_stopping = 80
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[experience]
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gpu_n_episodes = 64
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gpu_timesteps_per_episode = 200
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min_hold_bars = 3
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[experience.fill_simulation]
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ioc_fill_prob = 0.85
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limit_fill_min = 0.30
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limit_fill_max = 0.80
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spread_cost_frac = 0.50
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spread_capture_frac = 0.50
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[risk]
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max_position = 2.0
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loss_aversion = 1.5
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q_clip_min = -50.0
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q_clip_max = 50.0
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[advanced]
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n_steps = 3
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tau = 0.005
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c51_warmup_epochs = 5
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her_ratio = 0.2
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cql_alpha = 0.1
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curiosity_weight = 0.1
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iqn_lambda = 0.25
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spectral_norm_sigma_max = 1.5
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gradient_clip_norm = 1.0
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[reward]
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loss_aversion = 1.5
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q_gap_threshold = 0.1
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