{"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 0x7f4a6a102290>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f4a6a102320>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f4a6a1023b0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f4a6a102440>", "_build": "<function ActorCriticPolicy._build at 0x7f4a6a1024d0>", "forward": "<function ActorCriticPolicy.forward at 0x7f4a6a102560>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f4a6a1025f0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f4a6a102680>", "_predict": "<function ActorCriticPolicy._predict at 0x7f4a6a102710>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f4a6a1027a0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f4a6a102830>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f4a6a1028c0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f4a6a0f6680>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1683328261489613020, "learning_rate": 0.0, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.015808000000000044, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 248, "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:": "gAWVxgEAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUtDQwRkAVMAlE5HAAAAAAAAAACGlCmMAV+UhZSMHzxpcHl0aG9uLWlucHV0LTEwLTlhNzE0YTgxZWY3Mj6UjAg8bGFtYmRhPpRLDkMCBACUKSl0lFKUfZQojAtfX3BhY2thZ2VfX5ROjAhfX25hbWVfX5SMCF9fbWFpbl9flHVOTk50lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaBZ9lH2UKGgTaA2MDF9fcXVhbG5hbWVfX5RoDYwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoFIwHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5ROjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True  True  True  True  True  True  True  True]", "bounded_above": "[ True  True  True  True  True  True  True  True]", "_shape": [8], "low": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]", "high": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]", "low_repr": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]", "high_repr": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]", "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "gAWV/QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgLjAJpOJSJiIeUUpQoSwNoD05OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.10.11", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.0.0+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}