| {"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 0x7e25751e56c0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7e25751e5760>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7e25751e5800>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7e25751e58a0>", "_build": "<function ActorCriticPolicy._build at 0x7e25751e5940>", "forward": "<function ActorCriticPolicy.forward at 0x7e25751e59e0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7e25751e5a80>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7e25751e5b20>", "_predict": "<function ActorCriticPolicy._predict at 0x7e25751e5bc0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7e25751e5c60>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7e25751e5d00>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7e25751e5da0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7e2575185700>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1753620939574169247, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdgIAAAAAAACME251bXB5Ll9jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWAAIAAAAAAACacWm9SokuPw8cmz2fSsy+A31QvTomPT0AAAAAAAAAAIAEN70p+FS6yvrENrH2rTHOoEA6rQ7rtQAAgD8AAIA/ZmaMuXs+jLr4sIc5KZh4NO3Rh7qDnJ24AACAPwAAgD8AHoy8bILfPhwmhj3ydqS+hxFZPSafDz0AAAAAAAAAAABsBLyP9mG6FjldtSR/GLBjW4u6iYaQNAAAgD8AAIA/s5vbPSpbeT4yX0y+6+yRvikxODwztf67AAAAAAAAAAAa9q+97tK3Pls1Ij4rJP2+m78FPoKSDz4AAAAAAAAAAJpRBL4jwyk/OAphPhEB+L4NcS48FgV6PQAAAAAAAAAAZp4svB8NzLkjITG6K2mVNDXMIDnjD085AACAPwAAgD/N2sK8KfxluoLqbLkv90i0MkEsuwQwizgAAIA/AACAP7OeSb6MUkM/qu+mPXaenL5DaYO+Ql6DPgAAAAAAAAAAzWJSPPaIZbqgiVWzTERer2OjfLuafL4zAACAPwAAgD9zzsC96e5zvBJIybytDmc8cX3WPft5Or0AAAAAAACAP+aJGz2Daia8EuF5vc1la77K7ko8rAgOPQAAgD8AAIA/gHC7PeovsT99Qns+eOEFv4prXj0HQKa9AAAAAAAAAADNZjO9oFUvP7YiMD6sdqS+d14hvWC74D0AAAAAAAAAAJSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////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": 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"}} |