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65 lines
1.2 KiB
YAML
65 lines
1.2 KiB
YAML
method: bayes
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metric:
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name: sweep/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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use_jax:
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value: true
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# pmap requires all workers to compile the same computation graph shape,
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# so structural params are fixed -- only research/scalar params are swept
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algo:
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values: [ppo, a2c]
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jax_num_envs:
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value: 32
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jax_num_steps:
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value: 128
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jax_num_minibatches:
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value: 4
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jax_update_epochs:
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value: 4
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total_timesteps:
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value: 100000
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checkpoint_interval:
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value: 200000
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n_products:
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value: 10
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action_levels:
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value: 9
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# research parameters -- primary sweep targets
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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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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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# training hyperparameters
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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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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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