{"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 0x7be81ba1acb0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7be81ba1ad40>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7be81ba1add0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7be81ba1ae60>", "_build": "<function ActorCriticPolicy._build at 0x7be81ba1aef0>", "forward": "<function ActorCriticPolicy.forward at 0x7be81ba1af80>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7be81ba1b010>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7be81ba1b0a0>", "_predict": "<function ActorCriticPolicy._predict at 0x7be81ba1b130>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7be81ba1b1c0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7be81ba1b250>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7be81ba1b2e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7be81b9a2bc0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 835584, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1733479064306196761, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAOY1Sj1I/Y+6bqSyttGph7HsKDe72MfRNQAAgD8AAIA/MztOO+FEmrqSXrw5z4ZDtuapDLqzzNi4AACAPwAAgD+QR5k+s6FMP+qt5T0t6Ly+1x9mPhVvSrwAAAAAAAAAAA0GsT2BTFI+01xTvmCzU75yUou8yKfjvAAAAAAAAAAAMxMEvVyLFLqX/QK4AJl0srppKrqIFhY3AACAPwAAgD8aELa9XLdsuvgo1brer8Y00Wp4u3A19DkAAIA/AACAP4Mklz5/syk/RC02PZnCiL6AyUs+dwkfvQAAAAAAAAAAM00/PHuChLolfZq63JaPtb57AroeGrQ5AACAPwAAgD9NnbG9XGd3upcohTm8bhs1a5ohO/gkmLgAAIA/AACAP7rRJr4Nr5w+e1EOPQAKmL5l+EC93U5VvAAAAAAAAAAAluikvlu0MT9Qi4Q9XAXFvlJsP77HcAg+AAAAAAAAAADN/8G9w3VouMeIPLte0f+1phEXu02lYDoAAIA/AACAP2b2wDvcyjg/s05NPQOguL6JahY9lrGQPQAAAAAAAAAAMzCgPEj1hboC5aO73KcROIbzIzt2kqW2AACAPwAAgD+mKNK9SXJTPcXITzwWRlm+s1GtPMjOGb4AAAAAAAAAAPPzxb3hrIC6zUPeu5ScRjjPf446rcU5OAAAgD8AAIA/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksQSwiGlIwBQ5R0lFKULg=="}, "_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": 0.164416, "_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": 202, "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": 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:": "gAWVrQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuEQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUaACMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFowEZnVuY5SMDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz/JmZmZmZmahZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Thu Jun 27 21:05:47 UTC 2024", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.5.1+cu121", "GPU Enabled": "True", "Numpy": "1.26.4", "Cloudpickle": "3.1.0", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}