| {"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 0x7f5a57d96cb0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f5a57d96d40>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f5a57d96dd0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f5a57d96e60>", "_build": "<function ActorCriticPolicy._build at 0x7f5a57d96ef0>", "forward": "<function ActorCriticPolicy.forward at 0x7f5a57d96f80>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f5a57d97010>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f5a57d970a0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f5a57d97130>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f5a57d971c0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f5a57d97250>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f5a57d972e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f5a57d8f240>"}, "verbose": 0, "policy_kwargs": {}, "num_timesteps": 30720, "_total_timesteps": 30000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1685181150781332824, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAACq9kT6dD0W9U4TdPG8BVz0GHKm+wDcTPgAAgD8AAIA/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": -0.02400000000000002, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVWgYAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHwEhI9pyp71KMAWyUS/uMAXSUR0COvrgm7aqTdX2UKGgGR0BVX33xnWauaAdN6ANoCEdAjs5mseXAunV9lChoBkfARLpPuXu3MWgHS6FoCEdAjtC5uIhyKnV9lChoBkdAFRq+Jxeb/mgHS8toCEdAjtPe+/QBxXV9lChoBkdAPaIbfgrH2mgHS8JoCEdAjtc7ihnJ1nV9lChoBkfAYCQGMXJo02gHS8poCEdAjtrG4qgAZXV9lChoBkfANLriEQGwA2gHS6JoCEdAjt9jFAE+xHV9lChoBkfAUm8XrMTviWgHS/JoCEdAjuOLZ8KG+XV9lChoBkfAFGIRywOe8WgHS8BoCEdAjua/Aj6eoXV9lChoBkfAV0bSBshxHWgHS+9oCEdAjupxA0Kqn3V9lChoBkdAT8fv2GqPwWgHTegDaAhHQI73GTaCcwx1fZQoaAZHwDzW/etSydFoB0u/aAhHQI75LXcxj8V1fZQoaAZHQFkRp48lolFoB03oA2gIR0CPBgHuZ1FIdX2UKGgGR0BU/mPPszEaaAdN6ANoCEdAjxKesPrfL3V9lChoBkdAUaIONHYpUmgHS8RoCEdAjxX/I0ZWJnV9lChoBkdAJxEJa7mMfmgHS5loCEdAjxe5Aprk83V9lChoBkdAUa1jG1hLG2gHTegDaAhHQI8kSvicXnB1fZQoaAZHwCKmSMcZLqVoB0vqaAhHQI8m4swtapx1fZQoaAZHwGApCY9gWrRoB012AWgIR0CPKwhDgIhRdX2UKGgGR0An1x7zCk44aAdN6ANoCEdAjzd7F85S33V9lChoBkdASA3WOIZZS2gHTegDaAhHQI9HDbYbsGB1fZQoaAZHQBaEUTL4etFoB0vtaAhHQI9KsbDMvAZ1fZQoaAZHQEgwqzZ6D5FoB03oA2gIR0CPXY/s3Q2NdX2UKGgGR8BMZcp1A7gbaAdL+mgIR0CPYb3fQ8fWdX2UKGgGR8A7sSElE7W/aAdLzWgIR0CPY+55qubJdX2UKGgGR0BP4khq0tyxaAdN6ANoCEdAj3BgskIHDHV9lChoBkdANiGJJoTPB2gHS8BoCEdAj3KDU3GXHHV9lChoBkdAYfVAZ88cMmgHTQEDaAhHQI98YhhYvFp1fZQoaAZHwEGYro4dZJVoB0vSaAhHQI9+yuhbnox1fZQoaAZHQGCGWxptaZBoB03oA2gIR0CPiy33Hq/udX2UKGgGR0BTRNOZb6gvaAdN6ANoCEdAj5gFgUlAvHV9lChoBkdAXDYWSEDhcmgHTegDaAhHQI+knjMmnfl1fZQoaAZHQEJgKqGUOd5oB0u6aAhHQI+mtiH6/It1fZQoaAZHQFQPpNbkfcNoB03oA2gIR0CPtP9d/rjYdX2UKGgGR0BYdS75Ec81aAdN6ANoCEdAj8aZ0r9VFXV9lChoBkdAJ5UoKD0162gHS+RoCEdAj8qJokAxSHV9lChoBkdAVKQXGff4y2gHTegDaAhHQI/Z+tbLU1B1fZQoaAZHQATDj7yhBZ9oB0vIaAhHQI/cNgv114h1fZQoaAZHv/+xMFlkH2RoB0vKaAhHQI/frF2mpER1fZQoaAZHQF5Z46wMYuVoB03oA2gIR0CP7EaUiY9gdX2UKGgGR8A19RLsa86FaAdLzmgIR0CP7pOfukULdX2UKGgGR0BgzbSuyNXHaAdN6ANoCEdAj/uESVW0Z3V9lChoBkfASgttVJcxCmgHS9JoCEdAj/3ldC3PRnV9lChoBkfAOeCtFKCg9WgHS8FoCEdAkAANbHIZInV9lChoBkdAVP0r08NhE2gHTegDaAhHQJAGUPCl7+l1fZQoaAZHQCPOPkq+ajNoB0vSaAhHQJAHeexwAEN1fZQoaAZHQGNYHQyAQQNoB03oA2gIR0CQDcVDa4+bdX2UKGgGR0BQP4vBacI7aAdN6ANoCEdAkBUBZU1hs3V9lChoBkdAV2/YvnKW9mgHTegDaAhHQJAd9w5vLox1fZQoaAZHQF2l0bLlmvpoB03oA2gIR0CQJliyprDZdX2UKGgGR0BN2RL0z0pWaAdN6ANoCEdAkCyA9eQdS3V9lChoBkdAVtbD4xk/bGgHTegDaAhHQJAyzVVghKV1ZS4="}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 1392, "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": 1, "n_steps": 1024, "gamma": 0.995, "gae_lambda": 0.985, "ent_coef": 0.04, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 128, "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:": "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"}, "system_info": {"OS": "Linux-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023", "Python": "3.10.11", "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"}} |