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pendulum_crr:
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env: 'Pendulum-v1'
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run: CRR
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stop:
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evaluation/episode_reward_mean: -200
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training_iteration: 500
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config:
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input:
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- 'tests/data/pendulum/pendulum_replay_v1.1.0.zip'
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framework: torch
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gamma: 0.99
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train_batch_size: 1024
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critic_hidden_activation: 'relu'
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critic_hiddens: [256, 256]
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critic_lr: 0.0003
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actor_hidden_activation: 'relu'
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actor_hiddens: [256, 256]
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actor_lr: 0.0003
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actions_in_input_normalized: True
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clip_actions: True
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# Q function update setting
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twin_q: True
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target_update_grad_intervals: 1
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tau: 0.0001
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# evaluation
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evaluation_config:
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explore: False
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input: sampler
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evaluation_duration: 10
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evaluation_duration_unit: episodes
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evaluation_interval: 1
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evaluation_num_workers: 1
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evaluation_parallel_to_training: True
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# replay buffer
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replay_buffer_config:
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type: ray.rllib.utils.replay_buffers.MultiAgentReplayBuffer
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learning_starts: 0
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capacity: 100000
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# specific to CRR
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temperature: 1.0
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2022-06-08 02:42:02 -07:00
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weight_type: exp
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advantage_type: max
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max_weight: 20.0
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n_action_sample: 4
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