{
    "policy_class": {
        ":type:": "<class 'abc.ABCMeta'>",
        ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
        "__module__": "stable_baselines3.common.policies",
        "__doc__": "\n    Policy class for actor-critic algorithms (has both policy and value prediction).\n    Used by A2C, PPO and the likes.\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 ortho_init: Whether to use or not orthogonal initialization\n    :param use_sde: Whether to use State Dependent Exploration or not\n    :param log_std_init: Initial value for the log standard deviation\n    :param full_std: Whether to use (n_features x n_actions) parameters\n        for the std instead of only (n_features,) when using gSDE\n    :param sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n        a positive standard deviation (cf paper). It allows to keep variance\n        above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n    :param squash_output: Whether to squash the output using a tanh function,\n        this allows to ensure boundaries when using gSDE.\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 ActorCriticPolicy.__init__ at 0x7f0f6102fca0>",
        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f0f6102fd30>",
        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f0f6102fdc0>",
        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f0f6102fe50>",
        "_build": "<function ActorCriticPolicy._build at 0x7f0f6102fee0>",
        "forward": "<function ActorCriticPolicy.forward at 0x7f0f6102ff70>",
        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f0f61034040>",
        "_predict": "<function ActorCriticPolicy._predict at 0x7f0f610340d0>",
        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f0f61034160>",
        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f0f610341f0>",
        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f0f61034280>",
        "__abstractmethods__": "frozenset()",
        "_abc_impl": "<_abc_data object at 0x7f0f6102c4b0>"
    },
    "verbose": 0,
    "policy_kwargs": {},
    "observation_space": {
        ":type:": "<class 'gym.spaces.box.Box'>",
        ":serialized:": "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",
        "dtype": "float32",
        "_shape": [
            8
        ],
        "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'>",
        ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
        "n": 4,
        "_shape": [],
        "dtype": "int64",
        "_np_random": null
    },
    "n_envs": 1,
    "num_timesteps": 2000896,
    "_total_timesteps": 2000000,
    "_num_timesteps_at_start": 0,
    "seed": null,
    "action_noise": null,
    "start_time": 1672281195535001986,
    "learning_rate": 0.0003,
    "tensorboard_log": null,
    "lr_schedule": {
        ":type:": "<class 'function'>",
        ":serialized:": "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"
    },
    "_last_obs": {
        ":type:": "<class 'numpy.ndarray'>",
        ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAEBwKD6Hhyg+480gvtkelL42CW09uxt2vQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="
    },
    "_last_episode_starts": {
        ":type:": "<class 'numpy.ndarray'>",
        ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="
    },
    "_last_original_obs": null,
    "_episode_num": 0,
    "use_sde": false,
    "sde_sample_freq": -1,
    "_current_progress_remaining": -0.00044800000000000395,
    "ep_info_buffer": {
        ":type:": "<class 'collections.deque'>",
        ":serialized:": "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"
    },
    "ep_success_buffer": {
        ":type:": "<class 'collections.deque'>",
        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
    },
    "_n_updates": 7816,
    "n_steps": 1024,
    "gamma": 0.999,
    "gae_lambda": 0.98,
    "ent_coef": 0.01,
    "vf_coef": 0.5,
    "max_grad_norm": 0.5,
    "batch_size": 64,
    "n_epochs": 4,
    "clip_range": {
        ":type:": "<class 'function'>",
        ":serialized:": "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"
    },
    "clip_range_vf": null,
    "normalize_advantage": true,
    "target_kl": null
}