| {"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 0x7b4000ebc5e0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7b4000ebc670>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7b4000ebc700>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7b4000ebc790>", "_build": "<function ActorCriticPolicy._build at 0x7b4000ebc820>", "forward": "<function ActorCriticPolicy.forward at 0x7b4000ebc8b0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7b4000ebc940>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7b4000ebc9d0>", "_predict": "<function ActorCriticPolicy._predict at 0x7b4000ebca60>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7b4000ebcaf0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7b4000ebcb80>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7b4000ebcc10>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7b4009d00640>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1716656528575976590, "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:": "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:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuEQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+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 Sun Apr 28 14:29:16 UTC 2024", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.3.0+cu121", "GPU Enabled": "True", "Numpy": "1.25.2", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}} |