testing the hub 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: 266.83 +/- 10.79
+      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 0x7dc3667bfc70>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7dc3667bfd00>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7dc3667bfd90>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7dc3667bfe20>", "_build": "<function ActorCriticPolicy._build at 0x7dc3667bfeb0>", "forward": "<function ActorCriticPolicy.forward at 0x7dc3667bff40>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7dc3665cc040>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7dc3665cc0d0>", "_predict": "<function ActorCriticPolicy._predict at 0x7dc3665cc160>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7dc3665cc1f0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7dc3665cc280>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7dc3665cc310>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7dc366765c00>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1717420735371604569, "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:": "gAWVLQwAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQHIpBsMy8BeMAWyUS/2MAXSUR0Ce/k1CgK4QdX2UKGgGR0BvLX9Nvfj0aAdNLQFoCEdAnv7TdYW+G3V9lChoBkdAc98+ueSSvGgHS+BoCEdAnv7lZ5iVjnV9lChoBkdAbzH+xW1c+2gHTXkBaAhHQJ7/wQkHD791fZQoaAZHQHNH5PRArx1oB00KAWgIR0Ce/9LlFMIvdX2UKGgGR0Bw/OEh7mdRaAdNowFoCEdAnwBgFC9h7XV9lChoBkdAb0TlnRLK3mgHTV4BaAhHQJ8A8C+10DF1fZQoaAZHQHJwpaJQ+EBoB00KAWgIR0CfAbNY8uBddX2UKGgGR0Bttsq4H5aeaAdNFQFoCEdAnwKKYJE6UHV9lChoBkdAck/Dr7fpEGgHTQMBaAhHQJ8EH9tMwlB1fZQoaAZHQHCMJjDsMRZoB0v3aAhHQJ8EmRfWtlt1fZQoaAZHQHCQqrmyPdVoB02oAWgIR0CfBMiz9jwydX2UKGgGR0Bw/WYF7laKaAdL+mgIR0CfBSFbmlqKdX2UKGgGR0BwpXv4M4LkaAdNCwFoCEdAnwYZaq0dBHV9lChoBkdAcBzOJtSAH2gHTXoBaAhHQJ8GLamGdqd1fZQoaAZHQHAdEU0vXbxoB02MAWgIR0CfBla3I+4cdX2UKGgGR0Bwb87ZFocraAdL82gIR0CfBsgiu+yrdX2UKGgGR0Bxz2WiUPhAaAdNDAFoCEdAnwcOfEn9enV9lChoBkdAcqZ0wJw84mgHS/xoCEdAnwe8MVk+YHV9lChoBkdAcZO4KQaJh2gHTSYBaAhHQJ8IEv+OwPl1fZQoaAZHQHHmg9aEBbRoB00SAWgIR0CfCLgfU4JedX2UKGgGR0Bw5aFGoaUBaAdNBAFoCEdAnwjUJ0GNaXV9lChoBkdAcYucAR02cmgHS/xoCEdAnwk+WfK6nXV9lChoBkdAcAFvDxb0OGgHS/loCEdAnwsl0DEFXHV9lChoBkdActrM7U5MlGgHS/JoCEdAnwtMXBP9DXV9lChoBkdAcR2uUD+zdGgHTYYBaAhHQJ8LXT5O8Ch1fZQoaAZHQG/ZP07KaG5oB0v4aAhHQJ8LnZ39rGl1fZQoaAZHQHMOLUG3WnVoB0vzaAhHQJ8LxYISlFd1fZQoaAZHQHF3QU+LWI5oB01bAWgIR0CfDKGTcIqtdX2UKGgGR0BwjEGcFyJbaAdNCAFoCEdAnw1JB5X2d3V9lChoBkdAcKdsvqTr3WgHTRMBaAhHQJ8Ni+IuXeF1fZQoaAZHQG3YvK+zt1JoB00MAWgIR0CfDhNjbzshdX2UKGgGR0Bw4yCI1tO3aAdNBAFoCEdAnw4oInjQzHV9lChoBkdAb3/qmj0tiGgHTSIBaAhHQJ8ONzKcNH91fZQoaAZHQHG5jcRDkU9oB00FAWgIR0CfD/M8YAKfdX2UKGgGR0BxOKkrPMSsaAdNJgFoCEdAnxAubAk9lnV9lChoBkdAcPJu3MINVmgHTT0BaAhHQJ8QeBun/DN1fZQoaAZHQHIvt8Rcu8NoB00rAWgIR0CfEbK4QSSNdX2UKGgGR0BxyPc/MW43aAdNYQFoCEdAnxLnVG0/nnV9lChoBkdAcDHc8TzunmgHTSEBaAhHQJ8T1ltj0+V1fZQoaAZHQHFJivxH5JtoB00XAWgIR0CfE+LPldTpdX2UKGgGR0ByvfMFEAo5aAdNGAFoCEdAnxQYzFdcB3V9lChoBkdAcCgwlByCF2gHTU4BaAhHQJ8VJnCfpUx1fZQoaAZHQHMmyydFvydoB0v2aAhHQJ8VhPj4pMJ1fZQoaAZHQHAiTyauwHJoB01YAWgIR0CfFauSfUWmdX2UKGgGR0BxZCFBY3efaAdL/mgIR0CfFdmx+rlvdX2UKGgGR0Bx3B6sySFHaAdNIQFoCEdAnxZAZ88cMnV9lChoBkdAcTvPDpC8e2gHTUMBaAhHQJ8WXGaQV9F1fZQoaAZHQHGiqW5Yoy9oB01bAWgIR0CfLBCu2Zy/dX2UKGgGR0BvISMglnh9aAdNPAFoCEdAnywXZwn6VXV9lChoBkdAcxG3cYZVGWgHS/RoCEdAnywjmKZUk3V9lChoBkdAcQfxxDLKWGgHTRoBaAhHQJ8tdRfnfVJ1fZQoaAZHQG/CaeoUBXFoB0v6aAhHQJ8u/sD4gzR1fZQoaAZHQHD7E+otL+RoB00uAWgIR0CfL4gIhQnAdX2UKGgGR0BtUUDIRywOaAdNmQFoCEdAnzFu9nK4hHV9lChoBkdAc4d/H5rP+mgHTSkBaAhHQJ8yLvv0AcV1fZQoaAZHQHHF8HGCI1toB00EAWgIR0CfMnXNke6qdX2UKGgGR0Bt4xtJnQIEaAdNOwFoCEdAnzKdQ9A5aXV9lChoBkdAcLdlzU7SzGgHS/hoCEdAnzMStA9mpXV9lChoBkdAcabiR4hUzmgHTWsBaAhHQJ80SHVPN3Z1fZQoaAZHQHHBcH0K7ZpoB01MAWgIR0CfNJzMibDudX2UKGgGR0BwnOU+s5n2aAdNQgFoCEdAnzTWrOqvNnV9lChoBkdAbZPG4I8hcWgHTS4BaAhHQJ8016dDpkh1fZQoaAZHQHJnlY+0PYpoB01PAWgIR0CfNVsNUfgadX2UKGgGR0ByVfYsd1dPaAdNEwFoCEdAnzWJw84ginV9lChoBkdAcQdJswco6WgHTRcBaAhHQJ81m+xnnMd1fZQoaAZHQHJabOAy2x9oB0v7aAhHQJ82F9hJAdJ1fZQoaAZHQHGLrXUYsNFoB01dAWgIR0CfN0lz2exwdX2UKGgGR0BvoO0JF9a2aAdL/GgIR0CfN0gYgq3FdX2UKGgGR0Bx9p6Tnq3WaAdL+GgIR0CfN42LpA2RdX2UKGgGR0BvCm+GoJiRaAdNCQFoCEdAnzo4OhCdBnV9lChoBkdAcdLwNsnAqWgHTSwBaAhHQJ86Xq2SdOJ1fZQoaAZHQHGWN6kZaV5oB00qAWgIR0CfOvDIBBAwdX2UKGgGR0ByjnEYO2AoaAdNGQFoCEdAnzsoCEHt4XV9lChoBkdAcdCz9CNS62gHTUQBaAhHQJ88Ax33Ycx1fZQoaAZHQHHSS5/b0vpoB00MAWgIR0CfPCY4yXUpdX2UKGgGR0Bxjfr/sE7oaAdNFwFoCEdAnzw1YyO7x3V9lChoBkdAbtPltCRfW2gHTQgBaAhHQJ88P8fms/91fZQoaAZHQHHDfzBhx5toB00iAWgIR0CfPP0VJtiydX2UKGgGR0BzIWGATZg5aAdNDgFoCEdAnz0xeTmnwXV9lChoBkdAcqp0Lc9GJGgHTRUBaAhHQJ89Vy1eBxx1fZQoaAZHQHNsxk7OmixoB00MAWgIR0CfPcIzWPLgdX2UKGgGR0BxGQIfKZDzaAdNNQFoCEdAnz4SVKPGQ3V9lChoBkdAcgE7GvOhTWgHTRQBaAhHQJ8/fN7jT8Z1fZQoaAZHQHKW2XHBDXxoB002AWgIR0CfQBnSOR1YdX2UKGgGR0BwTSjJuEVWaAdNPQFoCEdAn0BJWJaaC3V9lChoBkdAbzk7J4jbBWgHTRQBaAhHQJ9CTSE12q11fZQoaAZHQHGfqBZpztFoB00WAWgIR0CfQyan752ydX2UKGgGR0BwplgAp8WsaAdL9GgIR0CfQ0LQHAymdX2UKGgGR0BwQtCu2Zy/aAdL72gIR0CfRIRTjvNNdX2UKGgGR0Bw8tbzK9wnaAdNAQFoCEdAn0S+A3DNyHV9lChoBkdAcAld9Ujs2WgHTQIBaAhHQJ9FvLEDQqt1fZQoaAZHQG6Kh/iHZbpoB00+AWgIR0CfRhTTfBN3dX2UKGgGR0BwqOFmFrVOaAdNgwFoCEdAn0aBas6q83V9lChoBkdAc5Nohpxm02gHTVYBaAhHQJ9GrRoh6jZ1fZQoaAZHQHH7Aa3qiXZoB01WAWgIR0CfRt19v0iAdX2UKGgGR0Bx6tHWjGkvaAdNlQFoCEdAn0fV5jYqXnV9lChoBkdAb/GcLBsQ/WgHS/9oCEdAn0iesPrfL3V9lChoBkdAclWvNu+AVmgHS/9oCEdAn0jWHHmzSnV9lChoBkdAckc4ACGN72gHTV0BaAhHQJ9JGeJ53Tx1ZS4="}, "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:": "gAWV2wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlE51Yi4=", "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-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sun Apr 28 14:29:16 UTC 2024", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.3.0+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:bdae9ef73e677432f987f7a9d045321cc58d06c8f406b88cbafb991f56459298
+size 148056

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 0x7dc3667bfc70>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7dc3667bfd00>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7dc3667bfd90>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7dc3667bfe20>",
+        "_build": "<function ActorCriticPolicy._build at 0x7dc3667bfeb0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7dc3667bff40>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7dc3665cc040>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7dc3665cc0d0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7dc3665cc160>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7dc3665cc1f0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7dc3665cc280>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7dc3665cc310>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7dc366765c00>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1717420735371604569,
+    "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": 248,
+    "observation_space": {
+        ":type:": "<class 'gymnasium.spaces.box.Box'>",
+        ":serialized:": "gAWVdgIAAAAAAACMFGd5bW5hc2l1bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMDWJvdW5kZWRfYmVsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWCAAAAAAAAAABAQEBAQEBAZRoCIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksIhZSMAUOUdJRSlIwNYm91bmRlZF9hYm92ZZRoESiWCAAAAAAAAAABAQEBAQEBAZRoFUsIhZRoGXSUUpSMBl9zaGFwZZRLCIWUjANsb3eUaBEoliAAAAAAAAAAAAC0wgAAtMIAAKDAAACgwNsPScAAAKDAAAAAgAAAAICUaAtLCIWUaBl0lFKUjARoaWdolGgRKJYgAAAAAAAAAAAAtEIAALRCAACgQAAAoEDbD0lAAACgQAAAgD8AAIA/lGgLSwiFlGgZdJRSlIwIbG93X3JlcHKUjFtbLTkwLiAgICAgICAgLTkwLiAgICAgICAgIC01LiAgICAgICAgIC01LiAgICAgICAgIC0zLjE0MTU5MjcgIC01LgogIC0wLiAgICAgICAgIC0wLiAgICAgICBdlIwJaGlnaF9yZXBylIxTWzkwLiAgICAgICAgOTAuICAgICAgICAgNS4gICAgICAgICA1LiAgICAgICAgIDMuMTQxNTkyNyAgNS4KICAxLiAgICAgICAgIDEuICAgICAgIF2UjApfbnBfcmFuZG9tlE51Yi4=",
+        "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:": "gAWV2wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlE51Yi4=",
+        "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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"
+    }
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8ab032992da2838620c3a83417299e5c2f0881a7ab46715f7d08ce555bf13bb0
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9be2a585a820176329460abd255f8429ef50066502ee022c4dbde4da40d4a156
+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-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sun Apr 28 14:29:16 UTC 2024
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.3.0+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": 266.82538739999995, "std_reward": 10.793288839287047, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-06-03T14:11:41.886556"}