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https://github.com/vale981/ray
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4cbe13cdfd
Co-authored-by: Sven Mika <sven@anyscale.io> Co-authored-by: sven1977 <svenmika1977@gmail.com>
45 lines
1.3 KiB
YAML
45 lines
1.3 KiB
YAML
# Pendulum 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-cql:
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env: Pendulum-v0
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run: CQL
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stop:
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episode_reward_mean: -300
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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: torch
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#input: sampler
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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: false
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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: 1
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clip_actions: False
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normalize_actions: true
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evaluation_num_workers: 1
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evaluation_interval: 1
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metrics_smoothing_episodes: 5
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bc_iters: 0
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evaluation_config:
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input: sampler
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