{"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 0x77fcfd37b0a0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x77fcfd37b130>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x77fcfd37b1c0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x77fcfd37b250>", "_build": "<function ActorCriticPolicy._build at 0x77fcfd37b2e0>", "forward": "<function ActorCriticPolicy.forward at 0x77fcfd37b370>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x77fcfd37b400>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x77fcfd37b490>", "_predict": "<function ActorCriticPolicy._predict at 0x77fcfd37b520>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x77fcfd37b5b0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x77fcfd37b640>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x77fcfd37b6d0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x77fcfd50da00>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1001472, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1711045052861558768, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAFoq+D3DEUW8HjNdPQbliL04Jji88/SDOgAAgD8AAIA/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////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": -0.0014719999999999178, "_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": 4890, "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:": "gAWV2wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlE51Yi4=", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 1, "n_steps": 2048, "gamma": 0.99, "gae_lambda": 0.95, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 10, "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+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz8zqSowVTJhhZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "system_info": {"OS": "Linux-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.2.1+cu121", "GPU Enabled": "True", "Numpy": "1.25.2", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}