FinetunedPPO_5mil_steps_total/data

{
    "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 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 share_features_extractor: If True, the features extractor is shared between the policy and value networks.\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 0x7f46846ef490>",
        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f46846ef520>",
        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f46846ef5b0>",
        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f46846ef640>",
        "_build": "<function ActorCriticPolicy._build at 0x7f46846ef6d0>",
        "forward": "<function ActorCriticPolicy.forward at 0x7f46846ef760>",
        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f46846ef7f0>",
        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f46846ef880>",
        "_predict": "<function ActorCriticPolicy._predict at 0x7f46846ef910>",
        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f46846ef9a0>",
        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f46846efa30>",
        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f46846efac0>",
        "__abstractmethods__": "frozenset()",
        "_abc_impl": "<_abc._abc_data object at 0x7f46846f1800>"
    },
    "verbose": 1,
    "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:": "gAWViAAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
        "n": 4,
        "_shape": [],
        "dtype": "int64",
        "_np_random": null
    },
    "n_envs": 224,
    "num_timesteps": 1146880,
    "_total_timesteps": 1000000,
    "_num_timesteps_at_start": 0,
    "seed": null,
    "action_noise": null,
    "start_time": 1678696720962980979,
    "learning_rate": 0.0003,
    "tensorboard_log": null,
    "lr_schedule": {
        ":type:": "<class 'function'>",
        ":serialized:": "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"
    },
    "_last_obs": {
        ":type:": "<class 'numpy.ndarray'>",
        ":serialized:": "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"
    },
    "_last_episode_starts": {
        ":type:": "<class 'numpy.ndarray'>",
        ":serialized:": "gAWVUwEAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJbgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYkvghZSMAUOUdJRSlC4="
    },
    "_last_original_obs": null,
    "_episode_num": 0,
    "use_sde": false,
    "sde_sample_freq": -1,
    "_current_progress_remaining": -0.1468799999999999,
    "ep_info_buffer": {
        ":type:": "<class 'collections.deque'>",
        ":serialized:": "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"
    },
    "ep_success_buffer": {
        ":type:": "<class 'collections.deque'>",
        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
    },
    "_n_updates": 92,
    "n_steps": 1024,
    "gamma": 0.999,
    "gae_lambda": 0.3,
    "ent_coef": 0.01,
    "vf_coef": 0.5,
    "max_grad_norm": 0.5,
    "batch_size": 1024,
    "n_epochs": 4,
    "clip_range": {
        ":type:": "<class 'function'>",
        ":serialized:": "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"
    },
    "clip_range_vf": null,
    "normalize_advantage": true,
    "target_kl": null
}