Upload PPO LunarLander-v2 trained agent

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 285.49 +/- 24.64
+      value: 291.13 +/- 15.13
       name: mean_reward
       verified: false
 ---

config.json

@@ -1 +1 @@
-{"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 0x7f78a3ebf3a0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f78a3ebf430>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f78a3ebf4c0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f78a3ebf550>", "_build": "<function ActorCriticPolicy._build at 0x7f78a3ebf5e0>", "forward": "<function ActorCriticPolicy.forward at 0x7f78a3ebf670>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f78a3ebf700>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f78a3ebf790>", "_predict": "<function ActorCriticPolicy._predict at 0x7f78a3ebf820>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f78a3ebf8b0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f78a3ebf940>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f78a3ebf9d0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f78a3ebed40>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 65536, "_total_timesteps": 50000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1712583025533407493, "learning_rate": 0.0005248111544143684, "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": -0.3107200000000001, "_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": 1920, "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:": "gAWV9wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgLjAJpOJSJiIeUUpQoSwNoD05OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "n_steps": 1024, "gamma": 0.9980331419912533, "gae_lambda": 0.9732726375562474, "ent_coef": 0.0019277118336484737, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 32, "n_epochs": 10, "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-5.19.0-45-generic-x86_64-with-glibc2.31 # 46~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Wed Jun 7 15:06:04 UTC 20", "Python": "3.9.16", "Stable-Baselines3": "2.0.0a5", "PyTorch": "1.12.1+cu116", "GPU Enabled": "True", "Numpy": "1.23.4", "Cloudpickle": "2.2.0", "Gymnasium": "0.28.1"}}
+{"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 0x7f78a3ebf3a0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f78a3ebf430>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f78a3ebf4c0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f78a3ebf550>", "_build": "<function ActorCriticPolicy._build at 0x7f78a3ebf5e0>", "forward": "<function ActorCriticPolicy.forward at 0x7f78a3ebf670>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f78a3ebf700>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f78a3ebf790>", "_predict": "<function ActorCriticPolicy._predict at 0x7f78a3ebf820>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f78a3ebf8b0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f78a3ebf940>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f78a3ebf9d0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f78a3ebed40>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1712585739049453820, "learning_rate": 0.0005248111544143684, "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": -0.015808000000000044, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWV5QsAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQHJosIAwPAiMAWyUS5yMAXSUR0CxeCTL8rI6dX2UKGgGR0BzqohaC+URaAdLqWgIR0Cxd+g1ejVQdX2UKGgGR0BxMF7ngYP5aAdLh2gIR0Cxd+sMEzO5dX2UKGgGR0By0qYlY2bYaAdLvmgIR0CxeDOQdS2qdX2UKGgGR0BxdE13t8eCaAdLg2gIR0CxeDNbcGkfdX2UKGgGR0BzUFipeeFtaAdLsmgIR0Cxd/uUUwi8dX2UKGgGR0ByoDscABDHaAdLv2gIR0CxeEFmz0HydX2UKGgGR0Bz4d3s5XEJaAdLqGgIR0CxeEoZ62ORdX2UKGgGR0B0HByksSTRaAdLrGgIR0CxeFHL7oB8dX2UKGgGR0Bw4SmaYu01aAdLnGgIR0CxeD1rdnCgdX2UKGgGR0BzcmFoL5RCaAdLsmgIR0CxeKGVRk3CdX2UKGgGR0ByC0RradtmaAdLemgIR0CxeMDGHYYjdX2UKGgGR0ByQWro4dZJaAdLo2gIR0CxeOUgOjIrdX2UKGgGR0BweJe5WilBaAdLp2gIR0CxePPaxoqTdX2UKGgGR0BQlasZHd43aAdLbmgIR0CxeOp62OQydX2UKGgGR0Bw1Xslb/wRaAdLn2gIR0CxecDIJZ4fdX2UKGgGR0BzI8DEFW4maAdLvWgIR0CxecatLcsUdX2UKGgGR0ByMvYZl4C7aAdLomgIR0Cxecrb5/LDdX2UKGgGR0BxqP1M/QjVaAdLoGgIR0CxedQlKK51dX2UKGgGR0BxuUK+i8FqaAdLqmgIR0CxedPva11GdX2UKGgGR0Bwg/MwDeTFaAdLnWgIR0Cxed8fA9FGdX2UKGgGR0By+9sBQvYfaAdLrmgIR0CxeaFajesQdX2UKGgGR0BxThywOe8PaAdLrGgIR0CxeftYGMXKdX2UKGgGR0BxjqtcOby6aAdLs2gIR0Cxeb8zImw8dX2UKGgGR0BzHY8W9DhMaAdLoWgIR0Cxtf0fT1CgdX2UKGgGR0BxSJsi0OVgaAdLsGgIR0CxtqPCQ9zPdX2UKGgGR0BwiC/M4cWCaAdLn2gIR0CxtsgBtDUmdX2UKGgGR0BwVqKiwjdIaAdLpWgIR0CxtunDej20dX2UKGgGR0By2d4FA3UAaAdLwWgIR0Cxtv8L0BfbdX2UKGgGR0BxujaGpMpPaAdLvGgIR0CxtzL2criEdX2UKGgGR0Bwl/UZvUBoaAdLl2gIR0Cxt+l5GBnSdX2UKGgGR0By6UV6/qPfaAdLrWgIR0Cxt/DfR/mUdX2UKGgGR0BzpwVRDTjOaAdLrWgIR0Cxt/Rn8KoidX2UKGgGR0BxNKZPVNHpaAdLvGgIR0CxuBtld1MedX2UKGgGR0BxfSjnFHawaAdLsWgIR0Cxt9vM4cWCdX2UKGgGR0BxBqN2ki2VaAdLqmgIR0CxuBbHIZIhdX2UKGgGR0Bxzu5Zr56/aAdLumgIR0CxuCOsPrfMdX2UKGgGR0Bxb+q0dBBzaAdL32gIR0CxuKbFn7HidX2UKGgGR0Bydihg3LmqaAdL6mgIR0CxuMXlOoHcdX2UKGgGR0Bv9I+nqFAWaAdLtWgIR0CxuOFAiV0LdX2UKGgGR0ByuUPQOWjXaAdLsmgIR0CxuPg/gR9PdX2UKGgGR0Bz8qorFwT/aAdLxWgIR0CxuWLojfNzdX2UKGgGR0ByrI+u/1xsaAdLnmgIR0CxuTdsFdLQdX2UKGgGR0ByZXQ0GeMAaAdLiGgIR0CxubaaG5+ZdX2UKGgGR0Bu/Ds+mm+CaAdLiWgIR0Cxub04NqgzdX2UKGgGR0BvOzxI8QqaaAdLj2gIR0Cxuf8spXp4dX2UKGgGR0Bw80Qf6oETaAdLpmgIR0CxuirD2rXEdX2UKGgGR0By6VUdaMaTaAdLu2gIR0CxuoYcrAgxdX2UKGgGR0Bwtq+wkgOjaAdLs2gIR0CxurYH5aePdX2UKGgGR0ByPrjLjghsaAdLxWgIR0Cxuxu5nUUgdX2UKGgGR0ByClD+irT6aAdLqGgIR0Cxu2SMPz4DdX2UKGgGR0BxFjCUHIIXaAdLoGgIR0Cxu2mhmGucdX2UKGgGR0ByFtRtP558aAdLpGgIR0Cxu41wxWT5dX2UKGgGR0ByONVhkRSQaAdLqGgIR0Cxu/Vrl/6PdX2UKGgGR0BxEQQcxTKlaAdL6mgIR0CxvAvCl7+ldX2UKGgGR0BxQLnRsuWbaAdLtWgIR0Cxu+2NipeedX2UKGgGR0Bw+hRBNVR2aAdLlmgIR0CxvD53HJcPdX2UKGgGR0BwbRzp5eJIaAdLrWgIR0CxvEftMPBjdX2UKGgGR0BwmnMSsbNsaAdLiGgIR0CxvGnTI/7jdX2UKGgGR0Bwr0F1SwW4aAdLmGgIR0CxvEY+4b0fdX2UKGgGR0Bz80YGdI5HaAdLxmgIR0CxvJPLLZBcdX2UKGgGR0BxRlOdoWYXaAdNEgJoCEdAsbzpuUD+znV9lChoBkdAcRKaWom5UmgHS6FoCEdAsby6jtXxOXV9lChoBkdAb6taWX1J2GgHTcABaAhHQLG8/Y2Kl551fZQoaAZHQHEDWNJe3QVoB0uLaAhHQLG9IiZv1lJ1fZQoaAZHQHCc894eLehoB0ukaAhHQLG9Q7sv7Fd1fZQoaAZHQHOU1Pva11JoB0unaAhHQLG9ByuIRAd1fZQoaAZHQG+GUXP7el9oB0uKaAhHQLG9gamXPZ91fZQoaAZHQHCy8K5TZQJoB0uvaAhHQLG+F2TgVGl1fZQoaAZHQHNIS6xxDLNoB0unaAhHQLG+OGfwqiJ1fZQoaAZHQHOwizLOiWVoB0utaAhHQLG9+QC0WuZ1fZQoaAZHQHCucRL9MsZoB0ueaAhHQLG+fzr/sE91fZQoaAZHQHQeQo5PuXxoB0vGaAhHQLG+quIyj591fZQoaAZHQHN1IIfKZD1oB0unaAhHQLG+2lYEGJN1fZQoaAZHQHEDAMMI/qxoB0uTaAhHQLG/MBu4wyt1fZQoaAZHQHGJz+WGATZoB0u7aAhHQLG/TbmU4aR1fZQoaAZHQHNMJK8L8aZoB0u7aAhHQLG/ZuRs/IN1fZQoaAZHQG/4rqUu+RJoB0uhaAhHQLG/J/Tb3491fZQoaAZHQHK1KbnX/YJoB0u+aAhHQLG/nfAKv3d1fZQoaAZHQHFXarFOwgVoB0u1aAhHQLG/84qPOpt1fZQoaAZHQGMjeSKWLP5oB03oA2gIR0CxwEAkxASndX2UKGgGR0ByF0tDlYEGaAdLpmgIR0CxwDfbO/tZdX2UKGgGR0Bx+9J2+wkgaAdLqmgIR0CxwIfKp1ifdX2UKGgGR0Bxj4J4SpR5aAdNRgFoCEdAscCXBciW3XV9lChoBkdAcCJGtZFG5WgHS75oCEdAscCqaVlf7nV9lChoBkdAdBTSRKYiPmgHS7doCEdAscD3Q2MsH3V9lChoBkdAcLBhCMPz4GgHS55oCEdAscFPGvOhTXV9lChoBkdAc+mZccENfGgHS8doCEdAscIDDziCKHV9lChoBkdAc/+6bONYKmgHS8VoCEdAscIZrYXfqHV9lChoBkdAc/hSUTtb92gHS8FoCEdAscJlLHuJDXV9lChoBkdAccPlQMx46mgHS/RoCEdAscJcco6S1XV9lChoBkdAcW7RbKRuCWgHS+BoCEdAscKfEQ5FPXV9lChoBkdAcY6P1L8JlmgHS79oCEdAscK+1TisGXV9lChoBkdAczNtvGZNPGgHS6poCEdAscLDXvphW3V9lChoBkdAcuTr1uivgWgHTSkBaAhHQLHC9Md92HN1fZQoaAZHQHCcQNgBtDVoB0uYaAhHQLHC+Oqebut1fZQoaAZHQHPZISHuZ1FoB0u+aAhHQLHDZzcAR051fZQoaAZHQHJYnHq/ub9oB0ufaAhHQLHDdKr7wa11fZQoaAZHQHKYgKa5PM1oB0vGaAhHQLHDmgCfYjB1fZQoaAZHQHILk9dNWU9oB0vOaAhHQLHDXAlfJFN1ZS4="}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 2580, "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:": "gAWV9wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgLjAJpOJSJiIeUUpQoSwNoD05OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "n_steps": 1024, "gamma": 0.9980331419912533, "gae_lambda": 0.9732726375562474, "ent_coef": 0.0019277118336484737, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 32, "n_epochs": 10, "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-5.19.0-45-generic-x86_64-with-glibc2.31 # 46~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Wed Jun 7 15:06:04 UTC 20", "Python": "3.9.16", "Stable-Baselines3": "2.0.0a5", "PyTorch": "1.12.1+cu116", "GPU Enabled": "True", "Numpy": "1.23.4", "Cloudpickle": "2.2.0", "Gymnasium": "0.28.1"}}

ppo-LunarLander-v2-8.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4af90407690a96099bf59059eb6bdf4fd0890f7f250019e1c6bcbd819418afcf
+size 146880

ppo-LunarLander-v2-8/_stable_baselines3_version

@@ -0,0 +1 @@
+2.0.0a5

ppo-LunarLander-v2-8/data

@@ -0,0 +1,99 @@
+{
+    "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 0x7f78a3ebf3a0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f78a3ebf430>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f78a3ebf4c0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f78a3ebf550>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f78a3ebf5e0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f78a3ebf670>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f78a3ebf700>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f78a3ebf790>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f78a3ebf820>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f78a3ebf8b0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f78a3ebf940>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f78a3ebf9d0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f78a3ebed40>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1712585739049453820,
+    "learning_rate": 0.0005248111544143684,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAECZOr5cp5o/dpeFvs1yLr+KJFG+FkmzvgAAAAAAAAAAZmb/uddDWbv+7ye+KTEQvqkQTz3JVqA/AAAAAAAAgD+mfKY9tKfNPhXpjL5Cujy//uydPUjhTr4AAAAAAAAAANpmAr5N87Q+SLsMPqXECL+u5U++1fblPQAAAAAAAAAAsxxyPT7YHj9QDAG9EedFv8zoXT4AXHe8AAAAAAAAAABmLpe84cD8ujX2zL0a/z28IC4fvLLDkD4AAIA/AAAAAGDcR75vcQU/nftcPnFtTb+VEFa+MuRcPgAAAAAAAAAAgCFVvU+PJ7xKThc+iwSqu+kLIr0PRxi+AACAPwAAgD+AuMS9NDLUPbrJnj5rrxC/oIqAO3COFj4AAAAAAAAAAA0og74lJbM/Gv8Pv0FD+b6s75m+CpquvgAAAAAAAAAAZlh+PHExFDqTfT88hnQuPWg0/jmoNF08AACAPwAAgD8z6Kq9GxTqPa1sLD5qL/u+rVtbvU4e7j0AAAAAAAAAAAB7/7zhKI+6fUcbslqdhLCP5zG5DVG4MgAAgD8AAIA/zSJ6PEgD3rqdiaO5hV2KPKgI4TsWk3G9AACAPwAAgD8AUNQ7GCh7P9wLRTy7fn+/u7czPftDDD0AAAAAAAAAAKbb/T1Cs3s+zdDmvpH1Hr8w7ye9N0CFvgAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksQSwiGlIwBQ5R0lFKULg=="
+    },
+    "_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": -0.015808000000000044,
+    "_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": 2580,
+    "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:": "gAWV9wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgLjAJpOJSJiIeUUpQoSwNoD05OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
+        "n": "4",
+        "start": "0",
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 16,
+    "n_steps": 1024,
+    "gamma": 0.9980331419912533,
+    "gae_lambda": 0.9732726375562474,
+    "ent_coef": 0.0019277118336484737,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 32,
+    "n_epochs": 10,
+    "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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"
+    }
+}

ppo-LunarLander-v2-8/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8be715b98e31d57c8230990c75a124bedfe58cc3dc8421fd4311b4d6285ad22f
+size 88057

ppo-LunarLander-v2-8/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e689d4891cf8db2c51201748e6925760f846a56a4ea71a2a37cde70d2e4136f0
+size 43329

ppo-LunarLander-v2-8/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
+size 431

ppo-LunarLander-v2-8/system_info.txt

@@ -0,0 +1,8 @@
+- OS: Linux-5.19.0-45-generic-x86_64-with-glibc2.31 # 46~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Wed Jun 7 15:06:04 UTC 20
+- Python: 3.9.16
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 1.12.1+cu116
+- GPU Enabled: True
+- Numpy: 1.23.4
+- Cloudpickle: 2.2.0
+- Gymnasium: 0.28.1

results.json

@@ -1 +1 @@
-{"mean_reward": 285.4913534338806, "std_reward": 24.64496552226025, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-04-08T13:37:22.518567"}
+{"mean_reward": 291.13230261788567, "std_reward": 15.12937779887247, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-04-08T15:40:48.558361"}