ddpg-Pendulum-v1/data

{
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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 0x7ff821f6e9d0>",
        "_get_constructor_parameters": "<function TD3Policy._get_constructor_parameters at 0x7ff821f6ea60>",
        "make_actor": "<function TD3Policy.make_actor at 0x7ff821f6eaf0>",
        "make_critic": "<function TD3Policy.make_critic at 0x7ff821f6eb80>",
        "forward": "<function TD3Policy.forward at 0x7ff821f6ec10>",
        "_predict": "<function TD3Policy._predict at 0x7ff821f6eca0>",
        "set_training_mode": "<function TD3Policy.set_training_mode at 0x7ff821f6ed30>",
        "__abstractmethods__": "frozenset()",
        "_abc_impl": "<_abc._abc_data object at 0x7ff821f70c80>"
    },
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        "n_critics": 1
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        "high": "[1. 1. 8.]",
        "bounded_below": "[ True  True  True]",
        "bounded_above": "[ True  True  True]",
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    "ep_success_buffer": {
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        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
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    "_n_updates": 10000,
    "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: 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 0x7ff821f6a430>",
        "add": "<function ReplayBuffer.add at 0x7ff821f6a4c0>",
        "sample": "<function ReplayBuffer.sample at 0x7ff821f6a550>",
        "_get_samples": "<function ReplayBuffer._get_samples at 0x7ff821f6a5e0>",
        "__abstractmethods__": "frozenset()",
        "_abc_impl": "<_abc._abc_data object at 0x7ff821f61bc0>"
    },
    "replay_buffer_kwargs": {},
    "train_freq": {
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    "use_sde_at_warmup": false,
    "policy_delay": 1,
    "target_noise_clip": 0.0,
    "target_policy_noise": 0.1,
    "actor_batch_norm_stats": [],
    "critic_batch_norm_stats": [],
    "actor_batch_norm_stats_target": [],
    "critic_batch_norm_stats_target": []
}