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{
"policy_class": {
":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 ",
"__init__": "<function TD3Policy.__init__ at 0x7feaaed9e170>",
"_build": "<function TD3Policy._build at 0x7feaaed9e200>",
"_get_constructor_parameters": "<function TD3Policy._get_constructor_parameters at 0x7feaaed9e290>",
"make_actor": "<function TD3Policy.make_actor at 0x7feaaed9e320>",
"make_critic": "<function TD3Policy.make_critic at 0x7feaaed9e3b0>",
"forward": "<function TD3Policy.forward at 0x7feaaed9e440>",
"_predict": "<function TD3Policy._predict at 0x7feaaed9e4d0>",
"set_training_mode": "<function TD3Policy.set_training_mode at 0x7feaaed9e560>",
"__abstractmethods__": "frozenset()",
"_abc_impl": "<_abc_data object at 0x7feaaed9c1b0>"
},
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"n_critics": 1
},
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"bounded_above": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False True]",
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"buffer_size": 1,
"batch_size": 100,
"learning_starts": 10000,
"tau": 0.005,
"gamma": 0.98,
"gradient_steps": -1,
"optimize_memory_usage": false,
"replay_buffer_class": {
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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:\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 :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 0x7feaaf21bb90>",
"add": "<function ReplayBuffer.add at 0x7feaaf21bc20>",
"sample": "<function ReplayBuffer.sample at 0x7feaaed827a0>",
"_get_samples": "<function ReplayBuffer._get_samples at 0x7feaaed82830>",
"__abstractmethods__": "frozenset()",
"_abc_impl": "<_abc_data object at 0x7feaaf2735d0>"
},
"replay_buffer_kwargs": {},
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"use_sde_at_warmup": false,
"policy_delay": 1,
"target_noise_clip": 0.0,
"target_policy_noise": 0.1,
"_last_dones": {
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"remove_time_limit_termination": false
} |