2022-05-16 13:13:49 +02:00
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cql-halfcheetahbulletenv-v0:
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env: HalfCheetahBulletEnv-v0
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run: CQL
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pass_criteria:
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evaluation/episode_reward_mean: 400.0
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timesteps_total: 10000000
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stop:
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time_total_s: 3600
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config:
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# Use input produced by expert SAC algo.
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input: ["~/halfcheetah_expert_sac.zip"]
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actions_in_input_normalized: true
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soft_horizon: False
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horizon: 1000
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2022-05-22 18:58:47 +01:00
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q_model_config:
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2022-05-16 13:13:49 +02:00
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fcnet_activation: relu
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fcnet_hiddens: [256, 256, 256]
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2022-05-22 18:58:47 +01:00
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policy_model_config:
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2022-05-16 13:13:49 +02:00
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fcnet_activation: relu
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fcnet_hiddens: [256, 256, 256]
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tau: 0.005
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target_entropy: auto
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no_done_at_end: false
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n_step: 3
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rollout_fragment_length: 1
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2022-05-17 13:43:49 +02:00
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replay_buffer_config:
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type: MultiAgentReplayBuffer
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learning_starts: 256
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2022-05-16 13:13:49 +02:00
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train_batch_size: 256
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target_network_update_freq: 0
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min_train_timesteps_per_reporting: 1000
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optimization:
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actor_learning_rate: 0.0001
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critic_learning_rate: 0.0003
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entropy_learning_rate: 0.0001
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num_workers: 0
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num_gpus: 1
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metrics_smoothing_episodes: 5
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# CQL Configs
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min_q_weight: 5.0
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bc_iters: 20000
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temperature: 1.0
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num_actions: 10
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lagrangian: False
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# Switch on online evaluation.
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evaluation_interval: 3
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evaluation_parallel_to_training: true
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evaluation_num_workers: 1
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evaluation_duration: 10
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evaluation_duration_unit: episodes
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evaluation_config:
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input: sampler
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always_attach_evaluation_results: True
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