PPO LunarLander-v2 My First Model Upload

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: 266.95 +/- 16.31
+      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 0x7fa0d8f8f130>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fa0d8f8f1c0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fa0d8f8f250>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fa0d8f8f2e0>", "_build": "<function ActorCriticPolicy._build at 0x7fa0d8f8f370>", "forward": "<function ActorCriticPolicy.forward at 0x7fa0d8f8f400>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fa0d8f8f490>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fa0d8f8f520>", "_predict": "<function ActorCriticPolicy._predict at 0x7fa0d8f8f5b0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fa0d8f8f640>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fa0d8f8f6d0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fa0d8f8f760>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fa0d8f8a240>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1683258514025754661, "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": -0.015808000000000044, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWV9wsAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQHFSSjtXxOOMAWyUS9GMAXSUR0CeO6BsQ/X5dX2UKGgGR0BxCvz06HTJaAdL0GgIR0CePBGgi/widX2UKGgGR0BwpBREWqLkaAdL5GgIR0CePB0Z3s5XdX2UKGgGR0BwuPnB+F10aAdLumgIR0CePKtAcDKYdX2UKGgGR0BxsNq7AckuaAdL52gIR0CePgS5RTCMdX2UKGgGR0BxSfHvMKTjaAdLz2gIR0CePpHggow3dX2UKGgGR0Bx5kvGp++eaAdNIAFoCEdAnj63+hoM8nV9lChoBkdAcMQMF2V3U2gHS7RoCEdAnkD5i7TUiXV9lChoBkdAb5Gzi0fHP2gHS8RoCEdAnkGoQWepXXV9lChoBkdAcfntyPuG9GgHTQEBaAhHQJ5DJQMx46h1fZQoaAZHQHD56i9IwudoB0vqaAhHQJ5DddNWU8p1fZQoaAZHQG8xX9JjDsNoB0vcaAhHQJ5E5s+FDfF1fZQoaAZHQG5tBFNL129oB0vTaAhHQJ5FHpJPIn11fZQoaAZHQG9lD4YaYNRoB0vMaAhHQJ5FrzpX6qN1fZQoaAZHQG+oe5OJtSBoB01iAWgIR0CeSThfShJzdX2UKGgGR0Bx16925hBraAdL4WgIR0CeSaJokAxSdX2UKGgGR0Bks6Pn0TURaAdN6ANoCEdAnksXYUWVNnV9lChoBkdAY33tZ3cHnmgHTegDaAhHQJ5LNhE0BOp1fZQoaAZHQG+h7XxvvSdoB0vIaAhHQJ5MCyxA0Kt1fZQoaAZHQHC7b/KhcqxoB0u/aAhHQJ5NDRoh6jZ1fZQoaAZHQHISVZTyauxoB0v9aAhHQJ5OFO58Sf11fZQoaAZHQG60sniNsFdoB0vQaAhHQJ5OPwNLDht1fZQoaAZHQHA7M2eg+QloB0u2aAhHQJ5OSx/ustF1fZQoaAZHQHPiSdvsJIFoB0voaAhHQJ5RwdjoZAJ1fZQoaAZHQF0DhE0BOpNoB03oA2gIR0CeUjW9US7HdX2UKGgGR0BPQZprULDyaAdLqmgIR0CeUoZDzAerdX2UKGgGR0BxIkYJmdy1aAdNDwFoCEdAnlM1inYQKHV9lChoBkdAbvjbSqlxfmgHS9xoCEdAnlSbjghr33V9lChoBkdAcUkTLGJemmgHS7toCEdAnlTSRnvlVHV9lChoBkdAQQG/ag261GgHS6NoCEdAnlV127nPmnV9lChoBkdAcXQwcHWz4WgHS9doCEdAnlYM1jy4F3V9lChoBkdAcajVsUIsy2gHS81oCEdAnlaKeCkGinV9lChoBkdAcBmkKeCkGmgHS9JoCEdAnlhoMnZ00XV9lChoBkdAbuKqbSZ0CGgHS85oCEdAnlhjPOY6XHV9lChoBkdAbdRi3ocJdGgHS9VoCEdAnlic9wFTvXV9lChoBkdAcvvs3hn8K2gHS7RoCEdAnlrIC+10DHV9lChoBkdAb+nA/s3Q2WgHS9NoCEdAnlrohhYvFnV9lChoBkdAcLscVgx8D2gHS+toCEdAnlxDENvwVnV9lChoBkdAcUcOX3QD3mgHS9hoCEdAnl1LAUL2H3V9lChoBkdAYpyIVM23rmgHTegDaAhHQJ5eD6wdKdx1fZQoaAZHQG6rTIvJzT5oB0vlaAhHQJ5eRlkH2RJ1fZQoaAZHQHBRHd9Dx9ZoB0v9aAhHQJ5eeUzKs+51fZQoaAZHQHBNkk4WDYhoB0vkaAhHQJ5epRgqmTF1fZQoaAZHQHDKTbJwKjVoB0viaAhHQJ5e5hXr+o91fZQoaAZHQG8nME7nxKBoB0vXaAhHQJ5f+ZML4N91fZQoaAZHQHEvQudwvQFoB0vvaAhHQJ5gt+UhV2l1fZQoaAZHQHFiBnrY5DJoB0vJaAhHQJ5hoxWT5ft1fZQoaAZHQHFi/QBxPwdoB00YAWgIR0CeYj51eSjhdX2UKGgGR0BwvV47ihnKaAdNBgFoCEdAnmO5fICEH3V9lChoBkdAcYfnE2pAEGgHS/JoCEdAnmRPoq0+knV9lChoBkdAcYgMfA9FF2gHS8BoCEdAnmRvuPV/c3V9lChoBkdAcJ39mpVCHGgHS75oCEdAnmS/wy6+WXV9lChoBkdAcWS2v0RODmgHS+ZoCEdAnmTPhAGB4HV9lChoBkdAYRG6aLGaQWgHTegDaAhHQJ5mAiD/VAl1fZQoaAZHQG+YRPO6d2BoB00GAWgIR0CeZl2TgVGkdX2UKGgGR0ByTR35eqrBaAdL02gIR0CeZ2u1F6RhdX2UKGgGR0BxdWfOD8LsaAdNEQFoCEdAnmeOgL7XQXV9lChoBkdAYpTPnjhky2gHTegDaAhHQJ5n0BdUsFt1fZQoaAZHQHE4FYuCf6JoB0v4aAhHQJ5n30kGA091fZQoaAZHQHEzkdaMaS9oB0vCaAhHQJ5oV9srNGF1fZQoaAZHQGPKRW1c+q1oB03oA2gIR0CeaGoHs1KodX2UKGgGR0BvnC35N47jaAdL2mgIR0CeaH5kbxVidX2UKGgGR0BwKsdELH+7aAdLxGgIR0CeaY4M4LkTdX2UKGgGR0BxDglLOAy3aAdL2mgIR0CeasM1CPZJdX2UKGgGR0Byq8Ouq3mWaAdL+mgIR0CebBsvqTr3dX2UKGgGR0BtJlFYuCf6aAdL3WgIR0CebH1RceKbdX2UKGgGR0BzJaPIXCTEaAdNFwFoCEdAnmyMXenAI3V9lChoBkdAclBc9GI9DGgHTSMBaAhHQJ5tTIIWxhV1fZQoaAZHQG+pkTpPhydoB0vuaAhHQJ5tVG4I8hd1fZQoaAZHQHC1eXAuZkVoB0vDaAhHQJ5tXy4FzMl1fZQoaAZHQG/wDMNc4YJoB0vMaAhHQJ5uNpTMqz91fZQoaAZHQHFN3dO6/ZdoB0vvaAhHQJ5uSdYnv2J1fZQoaAZHQG/kdfkWAPNoB0vPaAhHQJ5uaDmKZUl1fZQoaAZHQHICSiEg4fhoB0v1aAhHQJ5ulAPd2xJ1fZQoaAZHQHBGVs1sLv1oB0v/aAhHQJ5vKF8G9pR1fZQoaAZHQG/U55qubI9oB0v1aAhHQJ5wvQswtap1fZQoaAZHQHB4J3xFy7xoB0vRaAhHQJ5w9MWXTmZ1fZQoaAZHQG8t7MX7+DRoB0u+aAhHQJ5yHZCfHxV1fZQoaAZHQHBklG0/nnxoB0veaAhHQJ5yrcsUZel1fZQoaAZHQHKSTwlSjxloB0vGaAhHQJ5zMWj45951fZQoaAZHQGQD4kmhM8JoB03oA2gIR0Cec6HdoFmndX2UKGgGR0Bw13JjlPrOaAdLwWgIR0CedALKFIuodX2UKGgGR0BuM5SFXaJzaAdL5WgIR0CedDKyfL9udX2UKGgGR0BwGBBWxQizaAdNCAFoCEdAnnRbMPjGUHV9lChoBkdAcA+RmbsniWgHS8loCEdAnnSb+5vtMXV9lChoBkdAcaV2dNFjNWgHS/doCEdAnnWVWKdhAnV9lChoBkdAcF7CzTnaFmgHS/VoCEdAnnWkp/gBLnV9lChoBkdAb5GrQw9JSWgHS7loCEdAnnY0hq0ty3V9lChoBkdAcoYFz+3pfWgHTR0BaAhHQJ53f6tT1kF1fZQoaAZHQHIEcvmHP/toB0vuaAhHQJ537ohY/3Z1fZQoaAZHQHIoUn1FpfxoB0vXaAhHQJ54YX1rZap1fZQoaAZHQHAr/gJkXk5oB0vBaAhHQJ55m+dsi0R1fZQoaAZHQHGsfH93r2RoB0vuaAhHQJ56OjM3ZPF1fZQoaAZHQHC9u5rgwXZoB00BAWgIR0CeelNPgvUSdX2UKGgGR0ByshaKUFB6aAdL5mgIR0Ceev+10DEFdX2UKGgGR0BwDvN7jT8YaAdN4QFoCEdAnnuqcqe9SXV9lChoBkdAbkW58Sf16GgHS/loCEdAnnvQuZkTYnV9lChoBkdAcFis90RvnGgHS75oCEdAnnwYzi0fHXV9lChoBkdAYnRvP1L8JmgHTegDaAhHQJ58v2kBS1p1fZQoaAZHQHDMA8wHqu9oB0vmaAhHQJ58vwqiGnJ1ZS4="}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 310, "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:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "n_steps": 2048, "gamma": 0.99, "gae_lambda": 0.95, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "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:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuEQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz8zqSowVTJhhZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.10.11", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.0.0+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}

ppo-LunarLander-V2-Ameya.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:27d346612165d727756f10802a1c39bd4d536073a2d64aedd1169daffd547288
+size 146654

ppo-LunarLander-V2-Ameya/_stable_baselines3_version

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

ppo-LunarLander-V2-Ameya/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 0x7fa0d8f8f130>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fa0d8f8f1c0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fa0d8f8f250>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fa0d8f8f2e0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fa0d8f8f370>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fa0d8f8f400>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fa0d8f8f490>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fa0d8f8f520>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fa0d8f8f5b0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fa0d8f8f640>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fa0d8f8f6d0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fa0d8f8f760>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7fa0d8f8a240>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1683258514025754661,
+    "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": -0.015808000000000044,
+    "_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": 310,
+    "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:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=",
+        "n": "4",
+        "start": "0",
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 16,
+    "n_steps": 2048,
+    "gamma": 0.99,
+    "gae_lambda": 0.95,
+    "ent_coef": 0.0,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 64,
+    "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-Ameya/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5888b4380d85041dcaf046f741fda968a29078056c3cefc0de4e3ba796e61a03
+size 87929

ppo-LunarLander-V2-Ameya/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b4192870dfd383f322996bdacd29fa33729c2423957738cfb85a72ec92b49dd6
+size 43329

ppo-LunarLander-V2-Ameya/pytorch_variables.pth

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

ppo-LunarLander-V2-Ameya/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.10.11
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.0.0+cu118
+- GPU Enabled: True
+- Numpy: 1.22.4
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

@@ -0,0 +1 @@
+{"mean_reward": 266.95339632130606, "std_reward": 16.309625700660774, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-05-05T04:39:26.543550"}