{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gASVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__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 sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\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 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 0x7fc359855170>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fc359855200>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fc359855290>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fc359855320>", "_build": "<function ActorCriticPolicy._build at 0x7fc3598553b0>", "forward": "<function ActorCriticPolicy.forward at 0x7fc359855440>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fc3598554d0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fc359855560>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fc3598555f0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fc359855680>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fc359855710>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fc3598fa150>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gASVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/v///4wKb3J0aG9faW5pdJSJjA9vcHRpbWl6ZXJfY2xhc3OUjBN0b3JjaC5vcHRpbS5ybXNwcm9wlIwHUk1TcHJvcJSTlIwQb3B0aW1pemVyX2t3YXJnc5R9lCiMBWFscGhhlEc/764UeuFHrowDZXBzlEc+5Pi1iONo8YwMd2VpZ2h0X2RlY2F5lEsAdXUu", "log_std_init": -2, "ortho_init": false, "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>", "optimizer_kwargs": {"alpha": 0.99, "eps": 1e-05, "weight_decay": 0}}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [28], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf inf inf]", "bounded_below": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False]", "bounded_above": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [8], "low": "[-1. -1. -1. -1. -1. -1. -1. -1.]", "high": "[1. 1. 1. 1. 1. 1. 1. 1.]", "bounded_below": "[ True  True  True  True  True  True  True  True]", "bounded_above": "[ True  True  True  True  True  True  True  True]", "_np_random": null}, "n_envs": 4, "num_timesteps": 2000000, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1663362453.9818287, "learning_rate": 0.00096, "tensorboard_log": "./tensorboard", "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gASVwQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsBSwFLE0MEiABTAJROhZQpjAFflIWUjEgvdXNyL2xvY2FsL2xpYi9weXRob24zLjcvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuAQwIAAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEgvdXNyL2xvY2FsL2xpYi9weXRob24zLjcvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUdU5OaACMEF9tYWtlX2VtcHR5X2NlbGyUk5QpUpSFlHSUUpSMHGNsb3VkcGlja2xlLmNsb3VkcGlja2xlX2Zhc3SUjBJfZnVuY3Rpb25fc2V0c3RhdGWUk5RoH32UfZQoaBZoDYwMX19xdWFsbmFtZV9flIwZY29uc3RhbnRfZm4uPGxvY2Fscz4uZnVuY5SMD19fYW5ub3RhdGlvbnNfX5R9lIwOX19rd2RlZmF1bHRzX1+UTowMX19kZWZhdWx0c19flE6MCl9fbW9kdWxlX1+UaBeMB19fZG9jX1+UTowLX19jbG9zdXJlX1+UaACMCl9tYWtlX2NlbGyUk5RHP091EE1VHWmFlFKUhZSMF19jbG91ZHBpY2tsZV9zdWJtb2R1bGVzlF2UjAtfX2dsb2JhbHNfX5R9lHWGlIZSMC4="}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gASVjAAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSwSFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDBAAAAACUdJRiLg=="}, "_last_original_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_episode_num": 0, "use_sde": true, "sde_sample_freq": -1, "_current_progress_remaining": 0.0, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gASVRAwAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQJ2OVj7Q9ieMAWyUTegDjAF0lEdAsDq8AEMb33V9lChoBkdAn8kw1FYuCmgHTegDaAhHQLA9Shm5Dqp1fZQoaAZHQJ1zXWVeKKpoB03oA2gIR0CwP4hzmwJPdX2UKGgGR0CcldKaoddWaAdN6ANoCEdAsEBk/RmbsnV9lChoBkdAnCRp4jbBXWgHTegDaAhHQLBDM6wMYuV1fZQoaAZHQJmuSPaL4vhoB03oA2gIR0CwRbywwCbMdX2UKGgGR0CZo6mnfl6raAdN6ANoCEdAsEgCJyhi9nV9lChoBkdAnBIA80UGmmgHTegDaAhHQLBI23R5TqB1fZQoaAZHQJkre9zwMH9oB03oA2gIR0CwS6r5ylvZdX2UKGgGR0CU8GN1yNn5aAdN6ANoCEdAsE4zN4Z/C3V9lChoBkdAlNNvwiJO32gHTegDaAhHQLBQbgtvn8t1fZQoaAZHQJOjl0U47zVoB03oA2gIR0CwUUclb/wRdX2UKGgGR0CZcY/xlQMyaAdN6ANoCEdAsFQFhy8zynV9lChoBkdAmqgt2C/XXmgHTegDaAhHQLBWlZf2K2t1fZQoaAZHQJWefJmukk9oB03oA2gIR0CwWNeObRWtdX2UKGgGR0CSh3AE+xGEaAdN6ANoCEdAsFm2p3os7XV9lChoBkdAmHx7uQZGa2gHTegDaAhHQLBcecZLqUx1fZQoaAZHQJwbZDArQPZoB03oA2gIR0CwXw/rOZ9edX2UKGgGR0CYOFRxLkCFaAdN6ANoCEdAsGFIrK/203V9lChoBkdAmijTt9hJAmgHTegDaAhHQLBiJr/bTMJ1fZQoaAZHQJvr3drO7g9oB03oA2gIR0CwZO1sUIszdX2UKGgGR0CebIqYqoZRaAdN6ANoCEdAsGdyciGFjHV9lChoBkdAnIWPIKc/dWgHTegDaAhHQLBpq2dNFjN1fZQoaAZHQJvLL6sQumJoB03oA2gIR0CwaoJX2dupdX2UKGgGR0CblylHSWqtaAdN6ANoCEdAsG1UA93bEnV9lChoBkdAlgaY3rD632gHTegDaAhHQLBv6GG21D11fZQoaAZHQJuqDgKnei1oB03oA2gIR0Cwci4S13MZdX2UKGgGR0CKXsnEVFhHaAdN6ANoCEdAsHMQKVpsXXV9lChoBkdAnyeaXOW0JGgHTegDaAhHQLB14IdELIB1fZQoaAZHQJzrPj/+85FoB03oA2gIR0CweHeC04R3dX2UKGgGR0CXil+FUQ05aAdN6ANoCEdAsHq4OTaCc3V9lChoBkdAno/EkKNQ02gHTegDaAhHQLB7mNu+AVh1fZQoaAZHQJvZ4V9F4LVoB03oA2gIR0CwfmRRqGlAdX2UKGgGR0CSG5Z3LV4HaAdN6ANoCEdAsIEHg4wRG3V9lChoBkdAlASEcbR4QmgHTegDaAhHQLCDSbYsd1d1fZQoaAZHQJPJy5nUUfxoB03oA2gIR0CwhCX2AXl9dX2UKGgGR0CcmJqioKlYaAdN6ANoCEdAsIb3cWTHKnV9lChoBkdAmtagz1schmgHTegDaAhHQLCJgaTwDvF1fZQoaAZHQJzX7rLQokRoB03oA2gIR0Cwi85mVZ9vdX2UKGgGR0CafYGG21D0aAdN6ANoCEdAsI0rF1jiGXV9lChoBkdAkb2DU3GXHGgHTegDaAhHQLCQTy8zyjJ1fZQoaAZHQJY0Mu3+dbxoB03oA2gIR0CwkujAeq7zdX2UKGgGR0CYkC0wJw85aAdN6ANoCEdAsJU5VfeDWnV9lChoBkdAmY4gnDziCWgHTegDaAhHQLCWGEH+qBF1fZQoaAZHQJ1plZMcp9ZoB03oA2gIR0CwmOKsMiKSdX2UKGgGR0CfKjIldC3PaAdN6ANoCEdAsJt1CVrylXV9lChoBkdAn3fb+98JD2gHTegDaAhHQLCdsnyd4FB1fZQoaAZHQJ4F9DUmUnpoB03oA2gIR0CwnorFn7HidX2UKGgGR0CgIsNX5nDjaAdN6ANoCEdAsKFS8cuJ13V9lChoBkdAngNZmAbyY2gHTegDaAhHQLCj4UBGQS11fZQoaAZHQJ2/f73wkPdoB03oA2gIR0Cwphp3os7NdX2UKGgGR0CUoafIS13MaAdN6ANoCEdAsKb3752yLXV9lChoBkdAmwItDYywfWgHTegDaAhHQLCpxiEQGwB1fZQoaAZHQJg2aUnogV5oB03oA2gIR0CwrFYzvZyudX2UKGgGR0CbOjJCSidraAdN6ANoCEdAsK6QaZQYUHV9lChoBkdAmYozIzWPLmgHTegDaAhHQLCvbrUb1h91fZQoaAZHQJegCa7VawFoB03oA2gIR0CwsjtJFspHdX2UKGgGR0CcTPxFy7wsaAdN6ANoCEdAsLTNBWxQi3V9lChoBkdAnDxzYukDZGgHTegDaAhHQLC3EMINVip1fZQoaAZHQJxp8HQhOgxoB03oA2gIR0Cwt/D0pVjqdX2UKGgGR0Cen3OM2m52aAdN6ANoCEdAsLqykl/pdXV9lChoBkdAnB5IJE6T4mgHTegDaAhHQLC9Q46wMYx1fZQoaAZHQJqJa9sabWpoB03oA2gIR0Cwv4auW8h+dX2UKGgGR0CdtzGz8gp0aAdN6ANoCEdAsMBhRzijtXV9lChoBkdAmU2D6FdszmgHTegDaAhHQLDDJbnHNot1fZQoaAZHQJ+T/SLIgeRoB03oA2gIR0CwxblENOM3dX2UKGgGR0CXWxbkOqecaAdN6ANoCEdAsMf+qLjxTnV9lChoBkdAmvyNoBaLXWgHTegDaAhHQLDI2iLEUCd1fZQoaAZHQJ2NXYywfQtoB03oA2gIR0Cwy6PjwQUYdX2UKGgGR0Cb+zTXJ5miaAdN6ANoCEdAsM44QJ5VwXV9lChoBkdAnLTQgLZzxWgHTegDaAhHQLDQfWLP2PF1fZQoaAZHQJx/foePq9poB03oA2gIR0Cw0V6Xa8HwdX2UKGgGR0Cf3bgNwzciaAdN6ANoCEdAsNQhD0Dlo3V9lChoBkdAnsZ/ikwevWgHTegDaAhHQLDWsdwvQF91fZQoaAZHQJveeTgVGkNoB03oA2gIR0Cw2PYE0SAZdX2UKGgGR0Cf3fWqLjxTaAdN6ANoCEdAsNnV3JPqLXV9lChoBkdAm/y7PIGQjmgHTegDaAhHQLDcoOyE+Pl1fZQoaAZHQJ/6+UKRdQhoB03oA2gIR0Cw3zCSRr8BdX2UKGgGR0Cfwq1ejVQRaAdN6ANoCEdAsOFuzgMtsnV9lChoBkdAoSAZisny/mgHTegDaAhHQLDiTAlfJFN1fZQoaAZHQJ7IAE4ecQRoB03oA2gIR0Cw5RM7MgU2dX2UKGgGR0Ce9qpjtoi+aAdN6ANoCEdAsOegEU0vXnV9lChoBkdAoRA0Ku0TlGgHTegDaAhHQLDp1W5H3Dh1fZQoaAZHQKCRMK64DtBoB03oA2gIR0Cw6qmrGR3edX2UKGgGR0Cg5YgH3UQTaAdN6ANoCEdAsO1wMw1zhnV9lChoBkdAoGWgpazNU2gHTegDaAhHQLDv/g62fCh1fZQoaAZHQKDbKz67/XJoB03oA2gIR0Cw8j4pYs/ZdX2UKGgGR0CgryO8scyWaAdN6ANoCEdAsPMb5N47inV9lChoBkdAnwOedwvQGGgHTegDaAhHQLD15tEG7jF1fZQoaAZHQJ+1UGTs6aNoB03oA2gIR0Cw+HIDYAbRdX2UKGgGR0Cg69544ZMtaAdN6ANoCEdAsPrBXfZVXHV9lChoBkdAoUXTLbHp8mgHTegDaAhHQLD7m114gRt1fZQoaAZHQKB1HuQZGaxoB03oA2gIR0Cw/l/ub7TEdX2UKGgGR0ChZNguAZsLaAdN6ANoCEdAsQGjkdV/+nV9lChoBkdAoB6bl7tzCGgHTegDaAhHQLEED0NSZSh1fZQoaAZHQKCPxGgi/wloB03oA2gIR0CxBOja0x/NdX2UKGgGR0Cg9Ti4SYgJaAdN6ANoCEdAsQesjkdWAHV9lChoBkdAoIfw1k1/D2gHTegDaAhHQLEKRfm9xqB1fZQoaAZHQJ7kP7uUliVoB03oA2gIR0CxDITsY2sJdX2UKGgGR0CgVnCH6/IsaAdN6ANoCEdAsQ1nKaG5+nVlLg=="}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 62500, "n_steps": 8, "gamma": 0.99, "gae_lambda": 0.9, "ent_coef": 0.0, "vf_coef": 0.4, "max_grad_norm": 0.5, "normalize_advantage": false, "system_info": {"OS": "Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.7.14", "Stable-Baselines3": "1.6.0", "PyTorch": "1.12.1+cu113", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}