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        "set_training_mode": "<function TD3Policy.set_training_mode at 0x7f63c13eed30>",
        "__abstractmethods__": "frozenset()",
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    "_n_updates": 290668,
    "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    ",
        "__init__": "<function ReplayBuffer.__init__ at 0x7f63c13ea430>",
        "add": "<function ReplayBuffer.add at 0x7f63c13ea4c0>",
        "sample": "<function ReplayBuffer.sample at 0x7f63c13ea550>",
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        "__abstractmethods__": "frozenset()",
        "_abc_impl": "<_abc._abc_data object at 0x7f63c13e17c0>"
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    "replay_buffer_kwargs": {},
    "train_freq": {
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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": []
}