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45 lines
1.3 KiB
YAML
45 lines
1.3 KiB
YAML
# TransformedActionPendulum SAC can attain -150+ reward in 6-7k
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# Configurations are the similar to original softlearning/sac codebase
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pendulum-sac:
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env: ray.rllib.examples.env.transformed_action_space_env.TransformedActionPendulum
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run: SAC
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stop:
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episode_reward_mean: -500
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timesteps_total: 10000
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config:
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# Works for both torch and tf.
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framework: tf
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# Test, whether SAC is able to learn in "distorted" action spaces.
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env_config:
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config:
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low: 300.0
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high: 500.0
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horizon: 200
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soft_horizon: true
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Q_model:
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fcnet_activation: relu
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fcnet_hiddens: [256, 256]
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policy_model:
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fcnet_activation: relu
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fcnet_hiddens: [256, 256]
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tau: 0.005
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target_entropy: auto
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no_done_at_end: true
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n_step: 3
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rollout_fragment_length: 1
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prioritized_replay: true
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train_batch_size: 256
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target_network_update_freq: 1
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timesteps_per_iteration: 1000
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learning_starts: 256
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optimization:
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actor_learning_rate: 0.0003
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critic_learning_rate: 0.0003
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entropy_learning_rate: 0.0003
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num_workers: 0
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num_gpus: 0
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clip_actions: False
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normalize_actions: true
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metrics_smoothing_episodes: 5
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