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ars-Acrobot-v1-2627425996

Reinforcement Learning
stable-baselines3
Acrobot-v1
deep-reinforcement-learning
Eval Results
Model card Files Community
ars-Acrobot-v1-2627425996
File size: 13,382 Bytes 
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{
    "policy_class": {
        ":type:": "<class 'abc.ABCMeta'>",
        ":serialized:": "gAWVKgAAAAAAAACMGHNiM19jb250cmliLmFycy5wb2xpY2llc5SMCUFSU1BvbGljeZSTlC4=",
        "__module__": "sb3_contrib.ars.policies",
        "__doc__": "\n    Policy network for ARS.\n\n    :param observation_space: The observation space of the environment\n    :param action_space: The action space of the environment\n    :param net_arch: Network architecture, defaults to a 2 layers MLP with 64 hidden nodes.\n    :param activation_fn: Activation function\n    :param with_bias: If set to False, the layers will not learn an additive bias\n    :param squash_output: For continuous actions, whether the output is squashed\n        or not using a ``tanh()`` function. If not squashed with tanh the output will instead be clipped.\n    ",
        "__init__": "<function ARSPolicy.__init__ at 0x7f1b80515280>",
        "_get_constructor_parameters": "<function ARSPolicy._get_constructor_parameters at 0x7f1b80515310>",
        "forward": "<function ARSPolicy.forward at 0x7f1b805153a0>",
        "_predict": "<function ARSPolicy._predict at 0x7f1b80515430>",
        "__abstractmethods__": "frozenset()",
        "_abc_impl": "<_abc._abc_data object at 0x7f1b805175c0>"
    },
    "verbose": 1,
    "policy_kwargs": {
        "net_arch": [
            16
        ]
    },
    "observation_space": {
        ":type:": "<class 'gym.spaces.box.Box'>",
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        "dtype": "float32",
        "_shape": [
            6
        ],
        "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": {
        ":type:": "<class 'gym.spaces.discrete.Discrete'>",
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        "n": 3,
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        "_np_random": "RandomState(MT19937)"
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    "_total_timesteps": 500000,
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}