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"__doc__": "\n Policy class (with both actor and critic) for TD3.\n\n :param observation_space: Observation space\n :param action_space: Action space\n :param lr_schedule: Learning rate schedule (could be constant)\n :param net_arch: The specification of the policy and value networks.\n :param activation_fn: Activation function\n :param features_extractor_class: Features extractor to use.\n :param features_extractor_kwargs: Keyword arguments\n to pass to the features extractor.\n :param normalize_images: Whether to normalize images or not,\n dividing by 255.0 (True by default)\n :param optimizer_class: The optimizer to use,\n ``th.optim.Adam`` by default\n :param optimizer_kwargs: Additional keyword arguments,\n excluding the learning rate, to pass to the optimizer\n :param n_critics: Number of critic networks to create.\n :param share_features_extractor: Whether to share or not the features extractor\n between the actor and the critic (this saves computation time)\n ", |
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}, |
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"_n_updates": 290000, |
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"buffer_size": 1, |
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"batch_size": 100, |
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"learning_starts": 10000, |
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"tau": 0.005, |
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"gamma": 0.98, |
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"gradient_steps": 1, |
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"optimize_memory_usage": false, |
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"replay_buffer_class": { |
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"__module__": "stable_baselines3.common.buffers", |
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"__doc__": "\n Replay buffer used in off-policy algorithms like SAC/TD3.\n\n :param buffer_size: Max number of element in the buffer\n :param observation_space: Observation space\n :param action_space: Action space\n :param device: PyTorch device\n :param n_envs: Number of parallel environments\n :param optimize_memory_usage: Enable a memory efficient variant\n of the replay buffer which reduces by almost a factor two the memory used,\n at a cost of more complexity.\n See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195\n and https://github.com/DLR-RM/stable-baselines3/pull/28#issuecomment-637559274\n Cannot be used in combination with handle_timeout_termination.\n :param handle_timeout_termination: Handle timeout termination (due to timelimit)\n separately and treat the task as infinite horizon task.\n https://github.com/DLR-RM/stable-baselines3/issues/284\n ", |
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"__init__": "<function ReplayBuffer.__init__ at 0x7f9bab8ea430>", |
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"add": "<function ReplayBuffer.add at 0x7f9bab8ea4c0>", |
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"sample": "<function ReplayBuffer.sample at 0x7f9bab8ea550>", |
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"_get_samples": "<function ReplayBuffer._get_samples at 0x7f9bab8ea5e0>", |
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"__abstractmethods__": "frozenset()", |
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"_abc_impl": "<_abc._abc_data object at 0x7f9bab8e4680>" |
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}, |
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"replay_buffer_kwargs": {}, |
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"train_freq": { |
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":type:": "<class 'stable_baselines3.common.type_aliases.TrainFreq'>", |
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}, |
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"use_sde_at_warmup": false, |
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"policy_delay": 1, |
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"target_noise_clip": 0.0, |
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"target_policy_noise": 0.1, |
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"_action_repeat": [ |
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null |
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], |
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"surgeon": null, |
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"actor_batch_norm_stats": [], |
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"critic_batch_norm_stats": [], |
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"actor_batch_norm_stats_target": [], |
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"critic_batch_norm_stats_target": [], |
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"_last_action": { |
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":type:": "<class 'numpy.ndarray'>", |
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} |
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} |