| {"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 0x786fed592170>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x786fed592200>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x786fed592290>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x786fed592320>", "_build": "<function ActorCriticPolicy._build at 0x786fed5923b0>", "forward": "<function ActorCriticPolicy.forward at 0x786fed592440>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x786fed5924d0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x786fed592560>", "_predict": "<function ActorCriticPolicy._predict at 0x786fed5925f0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x786fed592680>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x786fed592710>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x786fed5927a0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x786f9009ab40>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1003520, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1734696206717366317, "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": -0.0035199999999999676, "_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": 315, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "gAWVdgIAAAAAAACMFGd5bW5hc2l1bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMDWJvdW5kZWRfYmVsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWCAAAAAAAAAABAQEBAQEBAZRoCIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksIhZSMAUOUdJRSlIwNYm91bmRlZF9hYm92ZZRoESiWCAAAAAAAAAABAQEBAQEBAZRoFUsIhZRoGXSUUpSMBl9zaGFwZZRLCIWUjANsb3eUaBEoliAAAAAAAAAAAAC0wgAAtMIAAKDAAACgwNsPScAAAKDAAAAAgAAAAICUaAtLCIWUaBl0lFKUjARoaWdolGgRKJYgAAAAAAAAAAAAtEIAALRCAACgQAAAoEDbD0lAAACgQAAAgD8AAIA/lGgLSwiFlGgZdJRSlIwIbG93X3JlcHKUjFtbLTkwLiAgICAgICAgLTkwLiAgICAgICAgIC01LiAgICAgICAgIC01LiAgICAgICAgIC0zLjE0MTU5MjcgIC01LgogIC0wLiAgICAgICAgIC0wLiAgICAgICBdlIwJaGlnaF9yZXBylIxTWzkwLiAgICAgICAgOTAuICAgICAgICAgNS4gICAgICAgICA1LiAgICAgICAgIDMuMTQxNTkyNyAgNS4KICAxLiAgICAgICAgIDEuICAgICAgIF2UjApfbnBfcmFuZG9tlE51Yi4=", "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": 1792, "gamma": 0.9967671781522843, "gae_lambda": 0.9952839743280041, "ent_coef": 0.004512600668404131, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 32, "n_epochs": 9, "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-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Thu Jun 27 21:05:47 UTC 2024", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.5.1+cu121", "GPU Enabled": "True", "Numpy": "1.26.4", "Cloudpickle": "3.1.0", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}} |