mirror of
https://github.com/vale981/ray
synced 2025-03-06 02:21:39 -05:00
39 lines
1.1 KiB
YAML
39 lines
1.1 KiB
YAML
# Our implementation of SAC can reach 9k reward in 400k timesteps
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halfcheetah_sac:
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env: HalfCheetah-v3
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run: SAC
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stop:
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episode_reward_mean: 9000
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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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horizon: 1000
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soft_horizon: false
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q_model_config:
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fcnet_activation: relu
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fcnet_hiddens: [256, 256]
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policy_model_config:
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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: 1
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rollout_fragment_length: 1
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train_batch_size: 256
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target_network_update_freq: 1
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min_sample_timesteps_per_reporting: 1000
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replay_buffer_config:
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type: MultiAgentPrioritizedReplayBuffer
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learning_starts: 10000
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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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evaluation_interval: 1
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metrics_smoothing_episodes: 5
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