| {"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 0x7a05944d8860>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7a05944d8900>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7a05944d89a0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7a05944d8a40>", "_build": "<function ActorCriticPolicy._build at 0x7a05944d8ae0>", "forward": "<function ActorCriticPolicy.forward at 0x7a05944d8b80>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7a05944d8c20>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7a05944d8cc0>", "_predict": "<function ActorCriticPolicy._predict at 0x7a05944d8d60>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7a05944d8e00>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7a05944d8ea0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7a05944d8f40>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7a05944393c0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1750378841658475526, "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:": "gAWVhAAAAAAAAACME251bXB5Ll9jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksQhZSMAUOUdJRSlC4="}, "_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:": "gAWV3AAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFm51bXB5Ll9jb3JlLm11bHRpYXJyYXmUjAZzY2FsYXKUk5SMBW51bXB5lIwFZHR5cGWUk5SMAmk4lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGJDCAQAAAAAAAAAlIaUUpSMBXN0YXJ0lGgIaA5DCAAAAAAAAAAAlIaUUpSMBl9zaGFwZZQpjAVkdHlwZZRoDowKX25wX3JhbmRvbZROdWIu", "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:": "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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-6.1.123+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sun Mar 30 16:01:29 UTC 2025", "Python": "3.11.13", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.6.0+cu124", "GPU Enabled": "True", "Numpy": "2.0.2", "Cloudpickle": "3.1.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}} |