| {"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 0x7fa165c1cc20>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fa165c1ccc0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fa165c1cd60>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fa165c1ce00>", "_build": "<function ActorCriticPolicy._build at 0x7fa165c1cea0>", "forward": "<function ActorCriticPolicy.forward at 0x7fa165c1cf40>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fa165c1cfe0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fa165c1d080>", "_predict": "<function ActorCriticPolicy._predict at 0x7fa165c1d120>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fa165c1d1c0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fa165c1d260>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fa165c1d300>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fa167e1ddc0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1750076002469388158, "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"}} |