duduvicky's picture
Upload PPO LunarLander-v2 trained agent
42cb838 verified
{"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 0x7b5c294859e0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7b5c29485a80>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7b5c29485b20>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7b5c29485bc0>", "_build": "<function ActorCriticPolicy._build at 0x7b5c29485c60>", "forward": "<function ActorCriticPolicy.forward at 0x7b5c29485d00>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7b5c29485da0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7b5c29485e40>", "_predict": "<function ActorCriticPolicy._predict at 0x7b5c29485ee0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7b5c29485f80>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7b5c29486020>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7b5c294860c0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7b5c2958fec0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1753303128305157824, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdgIAAAAAAACME251bXB5Ll9jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWAAIAAAAAAABNMzQ9H/2iueRTIDqTcjg1foXMOo+6PbkAAIA/AACAP828xzxI/5m6YEZmuwuxkbbyoKk4tYuEOgAAgD8AAIA/Ztq7vNK267s1ru07XjNqPK/IND01GUe9AACAPwAAgD9mQ2S9SB2OurUJB7z2OKK2YPktO8oiEjYAAIA/AACAP7PVdr0p8Cy6GtJSu4WxBDjKXBm60OZFtgAAgD8AAIA/ZtYRPeH2gbqFuTe4zqfGtF3IK7sm0VY3AACAPwAAgD+zI1E9DGmlPwZxrj0Qlqa+5dHNPWRkoTwAAAAAAAAAAL0td76sVJo+mi3VPeE6KL7QHGS9ar5MvAAAAAAAAAAAmo8rvCl4d7rvaj03qTdKMtzLMbplqV22AACAPwAAgD+aqL29QvoTPu+wkz33bme+wOs1PNEcyjsAAAAAAAAAAOZpHj2TJ38/ut02vaLqX77OUr88zV58vQAAAAAAAAAAgDwgvj1Ufru9AFm7ArWGuMyJtzx9QX46AACAPwAAgD+6w4I+wy57Pz4t1T5Q/pq+3up+PrY56j0AAAAAAAAAAJqxfDspIHi6ipiAOUWW8zTbXM069uaSuAAAgD8AAIA/2k+EvcNZHrpcPZS7moufN9dEvLfi32s6AACAPwAAgD9muuM7SGmWuuW6wbrcvbK1m1XpOr4p4DkAAIA/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": 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"}}