{"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 0x7ec8f233b5b0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ec8f233b640>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ec8f233b6d0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ec8f233b760>", "_build": "<function ActorCriticPolicy._build at 0x7ec8f233b7f0>", "forward": "<function ActorCriticPolicy.forward at 0x7ec8f233b880>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7ec8f233b910>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ec8f233b9a0>", "_predict": "<function ActorCriticPolicy._predict at 0x7ec8f233ba30>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ec8f233bac0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ec8f233bb50>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ec8f233bbe0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7ec8f233cd00>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1507328, "_total_timesteps": 1500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1689618318427706707, "learning_rate": 0.003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAABAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.004885333333333408, "_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": 460, "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:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "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": 128, "n_epochs": 5, "clip_range": {":type:": "<class 'function'>", ":serialized:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuEQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz/JmZmZmZmahZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuEQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz9ok3S8an76hZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "system_info": {"OS": "Linux-5.15.109+-x86_64-with-glibc2.31 # 1 SMP Fri Jun 9 10:57:30 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}