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85 lines
1.6 KiB
YAML
85 lines
1.6 KiB
YAML
method: random
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metric:
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name: objective/score
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goal: maximize
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command:
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- ${env}
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- python
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- -m
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- engine.train
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parameters:
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algo:
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values: [ppo, a2c, dqn, qtable]
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total_timesteps:
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values: [30000, 50000, 80000]
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seed:
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values: [13, 42, 77]
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n_products:
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values: [8, 10, 12]
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alpha:
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distribution: uniform
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min: 0.1
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max: 0.6
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lambda_coi:
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distribution: uniform
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min: 0.05
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max: 0.6
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robust_radius:
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distribution: uniform
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min: 0.0
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max: 0.3
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robust_points:
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values: [3, 5, 7]
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info_value:
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distribution: uniform
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min: 0.5
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max: 2.0
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revenue_weight:
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values: [0.005, 0.01, 0.02]
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learning_rate:
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distribution: log_uniform_values
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min: 1.0e-5
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max: 1.0e-3
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gamma:
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values: [0.97, 0.99, 0.995]
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buffer_size:
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values: [20000, 50000, 100000]
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batch_size:
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values: [128, 256, 512]
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tau:
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values: [0.002, 0.005, 0.01]
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train_freq:
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values: [1, 4, 8]
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learning_starts:
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values: [500, 1000, 3000]
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n_steps:
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values: [512, 1024, 2048]
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n_epochs:
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values: [5, 10, 20]
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gae_lambda:
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values: [0.9, 0.95, 0.98]
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clip_range:
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values: [0.1, 0.2, 0.3]
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ent_coef:
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values: [0.0, 0.005, 0.01]
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target_update_interval:
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values: [500, 1000, 2000]
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exploration_fraction:
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values: [0.1, 0.2, 0.3]
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exploration_final_eps:
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values: [0.01, 0.03, 0.05]
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action_levels:
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values: [7, 9, 11]
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action_scale_low:
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values: [0.75, 0.8, 0.85]
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action_scale_high:
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values: [1.15, 1.2, 1.25]
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q_lr:
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values: [0.03, 0.05, 0.1, 0.2]
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eps_start:
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value: 1.0
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eps_end:
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values: [0.02, 0.05, 0.1]
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eps_decay:
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values: [0.999, 0.9995, 0.9999]
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