Unload the first trained model

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: 171.16 +/- 94.12
+      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 0x7c3f11ac97e0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7c3f11ac9870>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7c3f11ac9900>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7c3f11ac9990>", "_build": "<function ActorCriticPolicy._build at 0x7c3f11ac9a20>", "forward": "<function ActorCriticPolicy.forward at 0x7c3f11ac9ab0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7c3f11ac9b40>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7c3f11ac9bd0>", "_predict": "<function ActorCriticPolicy._predict at 0x7c3f11ac9c60>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7c3f11ac9cf0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7c3f11ac9d80>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7c3f11ac9e10>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7c3f11a594c0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1000448, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1701330454555623643, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAOb7z732PDm6pTu1O2eq+TfWBdy6gFKmNQAAgD8AAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.00044800000000000395, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVNgwAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQF8WO4G2TgWMAWyUTegDjAF0lEdAnBU28yvcJ3V9lChoBkdAYTVz7uUliWgHTegDaAhHQJwcV0DEFW51fZQoaAZHQFuXc/+sHSpoB03oA2gIR0CcI71jy4FzdX2UKGgGR0A5oL1mJ3xGaAdNMQFoCEdAnCav5YYBNnV9lChoBkdAXl5IQOFxn2gHTegDaAhHQJwt0lkYoAp1fZQoaAZHQGAkz6SDAahoB03oA2gIR0CcNOoJAt4BdX2UKGgGR0BHvbrLQokSaAdNKAFoCEdAnDb/lIVdonV9lChoBkdAYNLvkRzzVmgHTegDaAhHQJxANCzC1qp1fZQoaAZHQFu1zEaVD8doB03oA2gIR0CcSHSCOFQEdX2UKGgGR0BiBkjHGS6laAdN6ANoCEdAnE+oHgP3BnV9lChoBkdAWpjdznzQNWgHTegDaAhHQJxW0MjNY8x1fZQoaAZHQEIrkMkQf6poB002AWgIR0CcWKsQNCqqdX2UKGgGR0BhanFR51NhaAdN6ANoCEdAnF/oNutOmHV9lChoBkdAMElARkEs8WgHTRkBaAhHQJxhmUGFBY51fZQoaAZHQGGKL3Cbc45oB03oA2gIR0CcaLafzz3AdX2UKGgGR0BgEsq8UVSGaAdN6ANoCEdAnHFAzpHI63V9lChoBkdAYA3ObAk9lmgHTegDaAhHQJx6qwX668R1fZQoaAZHQC0/tnf2saNoB0vLaAhHQJx71o4+8oR1fZQoaAZHwEOWgxrSE15oB0ujaAhHQJx9+IbfgrJ1fZQoaAZHQErbiAlOXVtoB00aAWgIR0Ccf5xbSqlxdX2UKGgGR0Bdqp6Uqx1QaAdN6ANoCEdAnIbbDAJswnV9lChoBkfAStS4BmwqzGgHS9loCEdAnIgsdxQzlHV9lChoBkfAHzJxeb/ff2gHS+RoCEdAnImLqMWGh3V9lChoBkfAOeth3JPqLWgHS4toCEdAnIpd4Z/CqXV9lChoBkfAPTSLl3hXKmgHS+JoCEdAnIzPQnhKlHV9lChoBkfAPAxxYJVsDWgHS7RoCEdAnI30o8ZDRnV9lChoBkdAXb9B6a9bo2gHTegDaAhHQJyVBcry1/l1fZQoaAZHQGF+QlByCFtoB03oA2gIR0CcnGPJq7AddX2UKGgGR0BB2oUBXCCSaAdLpWgIR0CcnWfxtpEhdX2UKGgGR8BHEmcnVoYfaAdL0mgIR0CcnpY287IUdX2UKGgGR0BcLP/3nIQwaAdN6ANoCEdAnKcFGkN4JXV9lChoBkdAYiPQu27Wd2gHTegDaAhHQJywd/mT1TR1fZQoaAZHwDXqPXCj1wpoB01cAWgIR0CcsrhXbM5fdX2UKGgGR0Ays+l0o0AMaAdNMQFoCEdAnLXXYUWVNnV9lChoBkdAYw/DkU9IPWgHTegDaAhHQJy8xDqnm7t1fZQoaAZHQDO3eHi3ocJoB00vAWgIR0Ccvo5xzaK2dX2UKGgGRz/u14gRsdkraAdNHgFoCEdAnMAtedCmdnV9lChoBkdAbUXYbsF+u2gHTZwBaAhHQJzDx/QSi/R1fZQoaAZHQGG7QOWjXWhoB03oA2gIR0CcyuV6NVBEdX2UKGgGR0BFr94eLehxaAdNaAFoCEdAnM0LMX7+DXV9lChoBkdAYDVzuF6Av2gHTegDaAhHQJzUJAJLM9t1fZQoaAZHQER8Pz4DcM5oB00qAWgIR0Cc1ezV+Zw5dX2UKGgGR0Bf+Qdfb9IgaAdN6ANoCEdAnN6JpFkQPXV9lChoBkdAYo7gRbr1NGgHTegDaAhHQJzoOvB7/n51fZQoaAZHQGBGj9XLeRBoB03oA2gIR0Cc739x6v7ndX2UKGgGR0BY3NEgGKQ8aAdN6ANoCEdAnPavrKNhmXV9lChoBkdAYRVUF0PpZGgHTegDaAhHQJz92d3B55Z1fZQoaAZHQF5iAOrhispoB03oA2gIR0CdBNcXFcY7dX2UKGgGR0BgT57w8W9EaAdN6ANoCEdAnQvs5bQkX3V9lChoBke/8+qvNeMQ3GgHS7ZoCEdAnQ0PbCaZyHV9lChoBkfARhF5+pfhM2gHS/RoCEdAnQ+Rjvuw5nV9lChoBkdARGYEKVpsXWgHTUwBaAhHQJ0RyXSjQAx1fZQoaAZHQBV3erMkhRtoB00KAWgIR0CdE8/4ZdfLdX2UKGgGR0BsjpVENOM3aAdNMgJoCEdAnRm8iGFi8XV9lChoBkdAaP0lDWsijmgHTYgBaAhHQJ0cyDEm6Xl1fZQoaAZHQGFZzOxB3RpoB03oA2gIR0CdJJswtapxdX2UKGgGR0Btpx3mmtQsaAdNSgFoCEdAnSac89wFT3V9lChoBkdAYJvuVopQUGgHTegDaAhHQJ0tzkkrwvx1fZQoaAZHQF0oPC2tuDVoB03oA2gIR0CdNUjzI3irdX2UKGgGR0BdPzsD4gzQaAdN6ANoCEdAnTyczAN5MXV9lChoBkdAXjlYEGJN02gHTegDaAhHQJ1D8rJ8v251fZQoaAZHQGgbGXPZ7HBoB01LAWgIR0CdRzWldkaudX2UKGgGR0BjPMZNwiqyaAdN6ANoCEdAnVDL1EmY0HV9lChoBkdAZRKrSVnmJWgHTegDaAhHQJ1ZHXrdFfB1fZQoaAZHQFNPYgq3EydoB03oA2gIR0CdYGSH/LkkdX2UKGgGR8Anlr9l2/zraAdNOQFoCEdAnWJNozvZy3V9lChoBkfAR7Ts4T9KmWgHS+FoCEdAnWOq8pTdcnV9lChoBkdANxwRXfZVXGgHTT8BaAhHQJ1lmLfk3jx1fZQoaAZHQGKd8Vgx8D1oB03oA2gIR0CdbMfU4JeFdX2UKGgGR0ArPamoBJZoaAdL/2gIR0Cdb3N47ihndX2UKGgGR0BgyTkELYwqaAdN6ANoCEdAnXaeWOZLI3V9lChoBkdAYdG3Zwn6VWgHTegDaAhHQJ192RxLkCF1fZQoaAZHwBiG+K0lZ5loB00jAWgIR0CdgDGR3eN2dX2UKGgGR8AcvsPatcOcaAdNKgFoCEdAnYKCx3V093V9lChoBkdAZAsSyt3fRGgHTegDaAhHQJ2L4G7jDKp1fZQoaAZHP/L0FKTSssBoB0u8aAhHQJ2NKIxgy/N1fZQoaAZHwDpJ18stkFxoB007AWgIR0CdkCNKyv9tdX2UKGgGR0Bhi1Pk7wKCaAdN6ANoCEdAnZdquSwGGHV9lChoBkdAYYC6YE4ecWgHTegDaAhHQJ2extj0+Tx1fZQoaAZHP/t/Tb349HNoB00JAWgIR0CdoFcQiA2AdX2UKGgGR0BenM9bHIZJaAdN6ANoCEdAnadmgWac7XV9lChoBkdAYWmKpDNQj2gHTegDaAhHQJ2vQwN9YwJ1fZQoaAZHQFumLPldTpBoB03oA2gIR0CdtySzPa+OdX2UKGgGR0BdQiHM2WIHaAdN6ANoCEdAncBYp+c6NnV9lChoBkdAamFh+fAbhmgHTbQDaAhHQJ3H7pLVWjp1fZQoaAZHQGr6qDbrTphoB02rA2gIR0CdztiqABkqdX2UKGgGR0BhIHzSThYOaAdN6ANoCEdAndZK4H5aeXV9lChoBkdAXiWmelKsdWgHTegDaAhHQJ3deZjQRf51fZQoaAZHQFlbxOtW+49oB03oA2gIR0Cd5I3gk1MudX2UKGgGR0Bc/Gbb1yvLaAdN6ANoCEdAneqqkyk9EHV9lChoBkdAbwu69TP0I2gHTZUCaAhHQJ3xXJDE3sJ1fZQoaAZHQG367tRekYZoB039AWgIR0Cd9rmKZUkwdX2UKGgGR0BuI9DMNc4YaAdNyAFoCEdAnfnyeI2wV3V9lChoBkdAa2mUgSvkimgHTVsCaAhHQJ3+tmpVCHB1fZQoaAZHQFzEoVEd/8VoB03oA2gIR0CeBg95Qgs9dX2UKGgGR0BUeWdNFjNIaAdN6ANoCEdAng08zuWrwXV9lChoBkdAa76rMC9ytGgHTfUBaAhHQJ4QJ1HOKO11fZQoaAZHQG5KqbBoEjhoB02UA2gIR0CeFsxesxO+dX2UKGgGR0BvJloL5RCQaAdN+QFoCEdAnhnGMju8b3V9lChoBkdAa8s28Zk08GgHTZ8BaAhHQJ4djKp1ifB1ZS4="}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 3908, "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": "Generator(PCG64)"}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "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", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": "Generator(PCG64)"}, "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:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuEQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz/JmZmZmZmahZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.1.0+cu118", "GPU Enabled": "True", "Numpy": "1.23.5", "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 sha256:14f6469dc91a99d8905958a153cd08ba2bb2f0b8810f8edb3a9757b7e8651838
+size 147864

ppo-LunarLander-v2/_stable_baselines3_version

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

ppo-LunarLander-v2/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 0x7c3f11ac97e0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7c3f11ac9870>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7c3f11ac9900>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7c3f11ac9990>",
+        "_build": "<function ActorCriticPolicy._build at 0x7c3f11ac9a20>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7c3f11ac9ab0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7c3f11ac9b40>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7c3f11ac9bd0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7c3f11ac9c60>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7c3f11ac9cf0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7c3f11ac9d80>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7c3f11ac9e10>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7c3f11a594c0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1000448,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1701330454555623643,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAOb7z732PDm6pTu1O2eq+TfWBdy6gFKmNQAAgD8AAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": null,
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": -0.00044800000000000395,
+    "_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": 3908,
+    "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": "Generator(PCG64)"
+    },
+    "action_space": {
+        ":type:": "<class 'gymnasium.spaces.discrete.Discrete'>",
+        ":serialized:": "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",
+        "n": "4",
+        "start": "0",
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": "Generator(PCG64)"
+    },
+    "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 sha256:4bb39f802b03e3f191bb6b0ea6dc61e259d38148d39bdae6a421c779f5365eae
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:beea4fe71b0e325dd0f584ef6d1a2cedf81d206fb9bd90cb5c5eb03a2c994e57
+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-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.1.0+cu118
+- GPU Enabled: True
+- Numpy: 1.23.5
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

@@ -0,0 +1 @@
+{"mean_reward": 171.1562313, "std_reward": 94.11870465043698, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-11-30T08:44:18.679753"}