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| ":type:": "<class 'abc.ABCMeta'>", | |
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| "__module__": "stable_baselines3.td3.policies", | |
| "__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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| "_build": "<function TD3Policy._build at 0x7f9551270b80>", | |
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| "make_critic": "<function TD3Policy.make_critic at 0x7f9551270d30>", | |
| "forward": "<function TD3Policy.forward at 0x7f9551270dc0>", | |
| "_predict": "<function TD3Policy._predict at 0x7f9551270e50>", | |
| "set_training_mode": "<function TD3Policy.set_training_mode at 0x7f9551270ee0>", | |
| "__abstractmethods__": "frozenset()", | |
| "_abc_impl": "<_abc._abc_data object at 0x7f9551659040>" | |
| }, | |
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| |
| }, | |
| "ep_success_buffer": { | |
| ":type:": "<class 'collections.deque'>", | |
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| }, | |
| "_n_updates": 990000, | |
| "buffer_size": 1, | |
| "batch_size": 256, | |
| "learning_starts": 10000, | |
| "tau": 0.005, | |
| "gamma": 0.99, | |
| "gradient_steps": 1, | |
| "optimize_memory_usage": false, | |
| "replay_buffer_class": { | |
| ":type:": "<class 'abc.ABCMeta'>", | |
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| "__module__": "stable_baselines3.common.buffers", | |
| "__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 ", | |
| "__init__": "<function ReplayBuffer.__init__ at 0x7f955126d5e0>", | |
| "add": "<function ReplayBuffer.add at 0x7f955126d670>", | |
| "sample": "<function ReplayBuffer.sample at 0x7f955126d700>", | |
| "_get_samples": "<function ReplayBuffer._get_samples at 0x7f955126d790>", | |
| "__abstractmethods__": "frozenset()", | |
| "_abc_impl": "<_abc._abc_data object at 0x7f9551264e00>" | |
| }, | |
| "replay_buffer_kwargs": {}, | |
| "train_freq": { | |
| ":type:": "<class 'stable_baselines3.common.type_aliases.TrainFreq'>", | |
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| }, | |
| "use_sde_at_warmup": false, | |
| "policy_delay": 2, | |
| "target_noise_clip": 0.5, | |
| "target_policy_noise": 0.2, | |
| "actor_batch_norm_stats": [], | |
| "critic_batch_norm_stats": [], | |
| "actor_batch_norm_stats_target": [], | |
| "critic_batch_norm_stats_target": [] | |
| } |