Upload PPO LunarLander-v2 trained agent

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+    metrics:
+    - type: mean_reward
+      value: 256.26 +/- 15.32
+      name: mean_reward
+      verified: false
+---
+
+# **PPO** Agent playing **LunarLander-v2**
+This is a trained model of a **PPO** agent playing **LunarLander-v2**
+using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
+
+## Usage (with Stable-baselines3)
+TODO: Add your code
+
+
+```python
+from stable_baselines3 import ...
+from huggingface_sb3 import load_from_hub
+
+...
+```

config.json

@@ -0,0 +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 0x7db6447d5510>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7db6447d55a0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7db6447d5630>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7db6447d56c0>", "_build": "<function ActorCriticPolicy._build at 0x7db6447d5750>", "forward": "<function ActorCriticPolicy.forward at 0x7db6447d57e0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7db6447d5870>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7db6447d5900>", "_predict": "<function ActorCriticPolicy._predict at 0x7db6447d5990>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7db6447d5a20>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7db6447d5ab0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7db6447d5b40>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7db644977280>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1708521119679433632, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": null, "_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": 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:": "gAWV/QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgLjAJpOJSJiIeUUpQoSwNoD05OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 1, "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-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.1.0+cu121", "GPU Enabled": "True", "Numpy": "1.25.2", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:00c1b4c1062e98886311f8838e7f04c55fa762003066f4f79806c0931173cacc
+size 147324

ppo-LunarLander-v2/_stable_baselines3_version

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

ppo-LunarLander-v2/data

@@ -0,0 +1,96 @@
+{
+    "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 0x7db6447d5510>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7db6447d55a0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7db6447d5630>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7db6447d56c0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7db6447d5750>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7db6447d57e0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7db6447d5870>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7db6447d5900>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7db6447d5990>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7db6447d5a20>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7db6447d5ab0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7db6447d5b40>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7db644977280>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1708521119679433632,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "_last_obs": null,
+    "_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:": "gAWVQQwAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQG3gnwob4rWMAWyUTSkBjAF0lEdAlzZtjwx33nV9lChoBkdAcODs3hn8K2gHTVUBaAhHQJc2lbKRuCR1fZQoaAZHQHI3kJSiudRoB01IAWgIR0CXNs2Pkq+bdX2UKGgGR0BuUv6KtPpIaAdNJwFoCEdAlzv1cMVk+XV9lChoBkdAccComois4mgHTTYBaAhHQJc8JSMtK7J1fZQoaAZHQG/YPhAGB4FoB00tAWgIR0CXUgFsYVIqdX2UKGgGR0Bugc7hegL7aAdNWwFoCEdAl1N1lPJq7HV9lChoBkdAcKiJTVDrq2gHTSABaAhHQJdUYSOBDoh1fZQoaAZHQHAA9vfj0cxoB01JAWgIR0CXVHywwCbMdX2UKGgGR0BxO8QAdXDFaAdNLwFoCEdAl1WfzWf9P3V9lChoBkdAby6mE4//vWgHTXoBaAhHQJdWg6GQCCB1fZQoaAZHQHGbVnuiN85oB01kAWgIR0CXVpnH/95ydX2UKGgGR0BwW5UaQ3glaAdNtwFoCEdAl1cZmZmZmnV9lChoBkdAcKItw71ZkmgHTcMBaAhHQJdXn212JSB1fZQoaAZHQGq8UutfXwtoB01+AWgIR0CXV9UpNKywdX2UKGgGR0BvQPzMA3kxaAdNbAFoCEdAl1fmBjFyaXV9lChoBkdAZXnf779AHGgHTegDaAhHQJdYgl9jPOZ1fZQoaAZHQHCYmCNCJGhoB02tAWgIR0CXWnCbMHKPdX2UKGgGR0BS0T5j6N2laAdN6ANoCEdAl1xTAaef7XV9lChoBkdAcUTm4y44ImgHTWoBaAhHQJddmvPkaMt1fZQoaAZHQHGkObqhUR5oB01tAWgIR0CXXdgHu7YkdX2UKGgGR0BxG/Uaya/iaAdNKQFoCEdAl19XpwCKaXV9lChoBkdAcOWlWwNb1WgHTWUBaAhHQJdftUaQ3gl1fZQoaAZHQHJgDhP0qYtoB01NAWgIR0CXX+BtDUmVdX2UKGgGR0Ax3XhfjS5RaAdNAQFoCEdAl1/0B4lhPXV9lChoBkdAcb8/TLGJemgHTUUBaAhHQJdhtrwe/6B1fZQoaAZHQHGzCTQmeDpoB01aAWgIR0CXYcqIacZtdX2UKGgGR0Bx9rVQQ+UyaAdNUAFoCEdAl2IruMMqjXV9lChoBkdAcTdBun/DL2gHTYYBaAhHQJdibGR3eN11fZQoaAZHQHCgfm1YyO9oB01SAWgIR0CXY1Y3eenRdX2UKGgGR0BwZ73fyf+TaAdNVwFoCEdAl2Nvcer+53V9lChoBkdAbFsWYWtU42gHTZwBaAhHQJdlge0Xxe91fZQoaAZHQHBmaHXVbzNoB00WAWgIR0CXZcFDv3JxdX2UKGgGR0BwKE42jwhGaAdNdQFoCEdAl2czDTBqK3V9lChoBkdAbPZrLyMDOmgHTSkBaAhHQJdnpwqAjIJ1fZQoaAZHQG/OKvvBrN5oB03KAWgIR0CXaBpI+W4WdX2UKGgGR0BwtFRVIZqEaAdNLAFoCEdAl2lzQVsUI3V9lChoBkdAcZQG7SRbKWgHTTQBaAhHQJdqGur6tT11fZQoaAZHQHByBJI1+ApoB01sAWgIR0CXaj4Fiay9dX2UKGgGR0By+dXOnl4kaAdNZAFoCEdAl2wIgJTl1nV9lChoBkdAcbm2mYSg5GgHTTYBaAhHQJdsS3solUp1fZQoaAZHQHGn4cFQl8hoB01uAWgIR0CXbEyTpxFRdX2UKGgGR0BxKHNxEORUaAdNTwFoCEdAl21+GXXyy3V9lChoBkdAcWX3iJfplmgHTSsBaAhHQJdtfsKLKmt1fZQoaAZHQHCovSlWOp9oB01YAWgIR0CXbfuKoAGTdX2UKGgGR0ByfBr9ETg3aAdNcgFoCEdAl243PVurInV9lChoBkdAUA+f/WDpT2gHS/doCEdAl2+BGpda+3V9lChoBkdAcBGz5XU6P2gHTXgBaAhHQJdwFyDIzWR1fZQoaAZHQHBx3cYZVGVoB001AWgIR0CXchDSgGr0dX2UKGgGR0BuQBEF4cFRaAdNLgFoCEdAl3Qfmgam43V9lChoBkdAcXQJxNqQBGgHTZ0BaAhHQJd0aScLBsR1fZQoaAZHQHKi8FEAo5RoB02qAWgIR0CXdK8+RoysdX2UKGgGR0A7lVrRBu4xaAdLyWgIR0CXdRJdSl3ydX2UKGgGR0BtHoIt16mgaAdNOgFoCEdAl3XS8J2MbXV9lChoBkdAceo20iQkomgHTX0BaAhHQJd2F79hqj91fZQoaAZHQHAF6L4vexhoB01BAWgIR0CXihbONYKZdX2UKGgGR0Bv7O+GoJiRaAdNTAFoCEdAl4rFMVUMonV9lChoBkdAa+E74i5d4WgHTYwBaAhHQJeK0fRu0kZ1fZQoaAZHQHBzEXk5p8FoB01uAWgIR0CXi/lUp/gBdX2UKGgGR0BtUbsF+uvEaAdNYQFoCEdAl4zba7EpAnV9lChoBkdAbyDxaPjn3mgHTUwBaAhHQJeM95v99+h1fZQoaAZHQG5rzTWoWHloB01bAWgIR0CXjTNGViWndX2UKGgGR0BwPj8/D+BIaAdNPAFoCEdAl42mD15B1XV9lChoBkdAbnRo9s7+1mgHTT0BaAhHQJeOJYEGJN11fZQoaAZHQHDrA9Net0VoB00UAWgIR0CXkCeyzHCGdX2UKGgGR0BqqF4oqkM1aAdNSwFoCEdAl5BSsKb8WXV9lChoBkdAcDlMzMzMzWgHTT4BaAhHQJeR8lD4QBh1fZQoaAZHQGv5lCswL3NoB01UAWgIR0CXk6dUsFt9dX2UKGgGR0BvAVoexOclaAdNfAFoCEdAl5QNknTiKnV9lChoBkdAbpKP0Zm7KGgHTSYBaAhHQJeUp50KZ2J1fZQoaAZHQEu4nfEXLvFoB0v6aAhHQJeVOCwr1/V1fZQoaAZHQHGJVb7j1f5oB02oAWgIR0CXlV8dPtUodX2UKGgGR0BrxFJL/S6UaAdNSQFoCEdAl5X9x6v7nHV9lChoBkdAcbh+aBqbjWgHTX0BaAhHQJeXKGZeAut1fZQoaAZHQHC6IBBAv+RoB01MAWgIR0CXl006HTJAdX2UKGgGR0A+Sqnm7rcCaAdNHAFoCEdAl5dn++/QB3V9lChoBkdAax25IYm9hGgHTUYBaAhHQJeYBJXhfjV1fZQoaAZHQG6lmfGuLaVoB00vAWgIR0CXmIrleWv9dX2UKGgGR0Bxumm51/2CaAdNfgFoCEdAl5oCzTnaFnV9lChoBkdAcGyAyVObiWgHTUMBaAhHQJebVPykKu11fZQoaAZHQHD3XWrfcetoB01VAWgIR0CXnA2wV0tAdX2UKGgGR0Bx58LncL0BaAdNOwFoCEdAl5zVImPYF3V9lChoBkdAa5YI1tO2zGgHTT8BaAhHQJeeekCV8kV1fZQoaAZHQHBZdKujh1loB007AWgIR0CXn9iNbTttdX2UKGgGR0Bx4heb/ffoaAdNWwFoCEdAl5/xeXzDoHV9lChoBkdAb2dd56dDpmgHTVYBaAhHQJegTXbuc+d1fZQoaAZHQHFhXQla8pVoB008AWgIR0CXoKgkTpPidX2UKGgGR0BxHPukUKzBaAdNVwFoCEdAl6D3L7oB73V9lChoBkdAcD22itaIN2gHTSgBaAhHQJehJ7gKnel1fZQoaAZHQG5eGlyimEZoB01IAWgIR0CXogSNfgJkdX2UKGgGR0Bx6aRigCfZaAdNQwFoCEdAl6KwB1cMVnV9lChoBkdAcByCHh0heWgHTXEBaAhHQJejwbp/wy91fZQoaAZHQHBWTEm6XjVoB01MAWgIR0CXo8ROUMXrdX2UKGgGR0BsEtgx8D0UaAdNXgFoCEdAl6bJsKsuF3V9lChoBkdAbbMOI68xsWgHTUYBaAhHQJeos75mAb11fZQoaAZHQGyn2sJY1YRoB01GAWgIR0CXqe3nIQvpdX2UKGgGR0Bwul7qptJnaAdNKwFoCEdAl6sO3Ytg8nV9lChoBkdAXSObutwJgWgHTegDaAhHQJess1UEPlN1fZQoaAZHQHID8RDkU9JoB01LAWgIR0CXrf6zmfXgdX2UKGgGR0Bu9wlIEr5JaAdNSAFoCEdAl65Ch37k4nVlLg=="
+    },
+    "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:": "gAWV/QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgLjAJpOJSJiIeUUpQoSwNoD05OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
+        "n": "4",
+        "start": "0",
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 1,
+    "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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"
+    }
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b19fde6077fda2a0446859d8a5bb33840bd24ddb0a36f758c725b9dffe2c51f5
+size 88490

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ca2799dbdce1e6bbfb1b23c273bb902cf51352a38d082ec71341b8160abaa923
+size 43762

ppo-LunarLander-v2/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0c35cea3b2e60fb5e7e162d3592df775cd400e575a31c72f359fb9e654ab00c5
+size 864

ppo-LunarLander-v2/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.1.0+cu121
+- GPU Enabled: True
+- Numpy: 1.25.2
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

@@ -0,0 +1 @@
+{"mean_reward": 256.2613064, "std_reward": 15.323303488534862, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-02-21T13:42:44.935557"}