| {"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 0x7c86393c7a60>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7c86393c7b00>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7c86393c7ba0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7c86393c7c40>", "_build": "<function ActorCriticPolicy._build at 0x7c86393c7ce0>", "forward": "<function ActorCriticPolicy.forward at 0x7c86393c7d80>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7c86393c7e20>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7c86393c7ec0>", "_predict": "<function ActorCriticPolicy._predict at 0x7c86393c7f60>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7c86393d4040>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7c86393d40e0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7c86393d4180>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7c863932ae80>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1755266062749358852, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdgIAAAAAAACME251bXB5Ll9jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWAAIAAAAAAAAwDqy+IAlHPyd6vD3aUtC+/uYxvsITLD0AAAAAAAAAAG0EOT40JeI+WqNOvetVjb6b1b09itdovQAAAAAAAAAAMwN9vE97bz4jpqo9YOmCvou0ID3BlpE7AAAAAAAAAAAAwr69gPnlPkNReTxtB8u+LLx2u6VOE70AAAAAAAAAAGYSNL3Ise89mEQqvfZNQ75I8QE7bgvhPAAAAAAAAAAAAPkLPaljJLwqrVS7KtQvPO1Lhj2muRq9AACAPwAAgD/mzVS9Jp2EP03sPb1HDMK+A7yRvUerOD0AAAAAAAAAAM0rCr2hhcy8y2H4PdSFjr6vmna9wVEdvwAAgD8AAIA/M8+BvFutfz9wEw69Gje8vosZy7294WA8AAAAAAAAAAAAjL88BpQuPzS7oj0iJam+Q9KPPKSTET0AAAAAAAAAADo+GL6/vwA+MGk0Podiar6VODY9C0iVvAAAAAAAAAAAzT8wvRQUrrriHDI6xNH+tBlZIjrz1Uu5AACAPwAAgD9m2UA9CzKfPweTlj5BFgS/4bpDPQ6ahD0AAAAAAAAAADOl67y/PIE/ZXoyvGs3v77v4569XP2uPQAAAAAAAAAAuiJwPhTbtD5rgoO+2o9yvkiycLwRNBm+AAAAAAAAAAAAg9k8UuG/ux29mrscVII8IQ0MPaLYXb0AAIA/AACAP5SMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGJLEEsIhpSMAUOUdJRSlC4="}, "_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": 250, "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"}} |