{"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 0x7dadf4fc65f0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7dadf4fc6680>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7dadf4fc6710>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7dadf4fc67a0>", "_build": "<function ActorCriticPolicy._build at 0x7dadf4fc6830>", "forward": "<function ActorCriticPolicy.forward at 0x7dadf4fc68c0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7dadf4fc6950>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7dadf4fc69e0>", "_predict": "<function ActorCriticPolicy._predict at 0x7dadf4fc6a70>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7dadf4fc6b00>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7dadf4fc6b90>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7dadf4fc6c20>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7dadf4fc8340>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1701706407123520442, "learning_rate": 0.0003, "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, "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": 64, "n_epochs": 4, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuEQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz8zqSowVTJhhZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "system_info": {"OS": "Linux-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.1.0+cu118", "GPU Enabled": "True", "Numpy": "1.23.5", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}