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        "__module__": "sb3_contrib.qrdqn.policies",
        "__doc__": "\n    Policy class with quantile and target networks for QR-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 n_quantiles: Number of quantiles\n    :param net_arch: The specification of the network architecture.\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 QRDQNPolicy.__init__ at 0x7fc6de66dc20>",
        "_build": "<function QRDQNPolicy._build at 0x7fc6de66dcb0>",
        "make_quantile_net": "<function QRDQNPolicy.make_quantile_net at 0x7fc6de66dd40>",
        "forward": "<function QRDQNPolicy.forward at 0x7fc6de66ddd0>",
        "_predict": "<function QRDQNPolicy._predict at 0x7fc6de66de60>",
        "_get_constructor_parameters": "<function QRDQNPolicy._get_constructor_parameters at 0x7fc6de66def0>",
        "set_training_mode": "<function QRDQNPolicy.set_training_mode at 0x7fc6de66df80>",
        "__abstractmethods__": "frozenset()",
        "_abc_impl": "<_abc_data object at 0x7fc6de66c1b0>"
    },
    "verbose": 1,
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        "net_arch": [
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            256
        ],
        "n_quantiles": 10,
        "optimizer_class": "<class 'torch.optim.adam.Adam'>",
        "optimizer_kwargs": {
            "eps": 0.00015625
        }
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        "dtype": "float32",
        "_shape": [
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        "low": "[-4.8000002e+00 -3.4028235e+38 -4.1887903e-01 -3.4028235e+38]",
        "high": "[4.8000002e+00 3.4028235e+38 4.1887903e-01 3.4028235e+38]",
        "bounded_below": "[ True  True  True  True]",
        "bounded_above": "[ True  True  True  True]",
        "_np_random": null
    },
    "action_space": {
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    "batch_size": 64,
    "learning_starts": 1000,
    "tau": 1.0,
    "gamma": 0.99,
    "gradient_steps": 128,
    "optimize_memory_usage": false,
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        "__init__": "<function ReplayBuffer.__init__ at 0x7fc6deec47a0>",
        "add": "<function ReplayBuffer.add at 0x7fc6deec4830>",
        "sample": "<function ReplayBuffer.sample at 0x7fc6deeb1830>",
        "_get_samples": "<function ReplayBuffer._get_samples at 0x7fc6deeb18c0>",
        "__abstractmethods__": "frozenset()",
        "_abc_impl": "<_abc_data object at 0x7fc6def2c060>"
    },
    "replay_buffer_kwargs": {},
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    "actor": null,
    "use_sde_at_warmup": false,
    "exploration_initial_eps": 1.0,
    "exploration_final_eps": 0.04,
    "exploration_fraction": 0.16,
    "target_update_interval": 10,
    "max_grad_norm": null,
    "exploration_rate": 0.04,
    "exploration_schedule": {
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    "n_quantiles": 10
}