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        "__module__": "stable_baselines3.dqn.policies",
        "__doc__": "\n    Policy class with Q-Value Net and target net for DQN\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    ",
        "__init__": "<function DQNPolicy.__init__ at 0x7f6263d219e0>",
        "_build": "<function DQNPolicy._build at 0x7f6263d21a70>",
        "make_q_net": "<function DQNPolicy.make_q_net at 0x7f6263d21b00>",
        "forward": "<function DQNPolicy.forward at 0x7f6263d21b90>",
        "_predict": "<function DQNPolicy._predict at 0x7f6263d21c20>",
        "_get_constructor_parameters": "<function DQNPolicy._get_constructor_parameters at 0x7f6263d21cb0>",
        "set_training_mode": "<function DQNPolicy.set_training_mode at 0x7f6263d21d40>",
        "__abstractmethods__": "frozenset()",
        "_abc_impl": "<_abc_data object at 0x7f6263d098d0>"
    },
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            256
        ]
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        "dtype": "float32",
        "_shape": [
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        ],
        "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
        "high": "[inf inf inf inf inf inf inf inf]",
        "bounded_below": "[False False False False False False False False]",
        "bounded_above": "[False False False False False False False False]",
        "_np_random": null
    },
    "action_space": {
        ":type:": "<class 'gym.spaces.discrete.Discrete'>",
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    "ep_success_buffer": {
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    "buffer_size": 1,
    "batch_size": 128,
    "learning_starts": 0,
    "tau": 1.0,
    "gamma": 0.99,
    "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:\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 0x7f62641987a0>",
        "add": "<function ReplayBuffer.add at 0x7f6264198830>",
        "sample": "<function ReplayBuffer.sample at 0x7f6264185830>",
        "_get_samples": "<function ReplayBuffer._get_samples at 0x7f62641858c0>",
        "__abstractmethods__": "frozenset()",
        "_abc_impl": "<_abc_data object at 0x7f6264200060>"
    },
    "replay_buffer_kwargs": {},
    "train_freq": {
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    "actor": null,
    "use_sde_at_warmup": false,
    "exploration_initial_eps": 1.0,
    "exploration_final_eps": 0.1,
    "exploration_fraction": 0.12,
    "target_update_interval": 250,
    "_n_calls": 100000,
    "max_grad_norm": 10,
    "exploration_rate": 0.1,
    "exploration_schedule": {
        ":type:": "<class 'function'>",
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}