Quentin Gallouédec

Initial commit

e424766 over 1 year ago

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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 0x7f3c13b6e940>",
	"_build": "<function TD3Policy._build at 0x7f3c13b6e9d0>",
	"_get_constructor_parameters": "<function TD3Policy._get_constructor_parameters at 0x7f3c13b6ea60>",
	"make_actor": "<function TD3Policy.make_actor at 0x7f3c13b6eaf0>",
	"make_critic": "<function TD3Policy.make_critic at 0x7f3c13b6eb80>",
	"forward": "<function TD3Policy.forward at 0x7f3c13b6ec10>",
	"_predict": "<function TD3Policy._predict at 0x7f3c13b6eca0>",
	"set_training_mode": "<function TD3Policy.set_training_mode at 0x7f3c13b6ed30>",
	"__abstractmethods__": "frozenset()",
	"_abc_impl": "<_abc._abc_data object at 0x7f3c140034c0>"
	},
	"verbose": 1,
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	"n_critics": 1
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	"high": "[inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf]",
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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]",
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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,
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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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	"add": "<function ReplayBuffer.add at 0x7f3c13b6a4c0>",
	"sample": "<function ReplayBuffer.sample at 0x7f3c13b6a550>",
	"_get_samples": "<function ReplayBuffer._get_samples at 0x7f3c13b6a5e0>",
	"__abstractmethods__": "frozenset()",
	"_abc_impl": "<_abc._abc_data object at 0x7f3c13b61780>"
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	"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,
	"_action_repeat": [
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	"surgeon": null,
	"actor_batch_norm_stats": [],
	"critic_batch_norm_stats": [],
	"actor_batch_norm_stats_target": [],
	"critic_batch_norm_stats_target": [],
	"_last_action": {
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	}