qrdqn-Acrobot-v1/data

{
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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 0x7f48962d3820>",
        "_build": "<function QRDQNPolicy._build at 0x7f48962d38b0>",
        "make_quantile_net": "<function QRDQNPolicy.make_quantile_net at 0x7f48962d3940>",
        "forward": "<function QRDQNPolicy.forward at 0x7f48962d39d0>",
        "_predict": "<function QRDQNPolicy._predict at 0x7f48962d3a60>",
        "_get_constructor_parameters": "<function QRDQNPolicy._get_constructor_parameters at 0x7f48962d3af0>",
        "set_training_mode": "<function QRDQNPolicy.set_training_mode at 0x7f48962d3b80>",
        "__abstractmethods__": "frozenset()",
        "_abc_impl": "<_abc._abc_data object at 0x7f48962d6800>"
    },
    "verbose": 1,
    "policy_kwargs": {
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        "net_arch": [
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            256
        ],
        "n_quantiles": 25,
        "optimizer_class": "<class 'torch.optim.adam.Adam'>",
        "optimizer_kwargs": {
            "eps": 7.8125e-05
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        "dtype": "float32",
        "_shape": [
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        "low": "[ -1.        -1.        -1.        -1.       -12.566371 -28.274334]",
        "high": "[ 1.        1.        1.        1.       12.566371 28.274334]",
        "bounded_below": "[ True  True  True  True  True  True]",
        "bounded_above": "[ True  True  True  True  True  True]",
        "_np_random": null
    },
    "action_space": {
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    "_total_timesteps": 100000,
    "_num_timesteps_at_start": 0,
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}