LunarLander-v2 uses the PP0 algorithm.

PPO-LunarLander-v2.zip

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 version https://git-lfs.github.com/spec/v1
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PPO-LunarLander-v2/_stable_baselines3_version

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-1.5.0
+1.6.0

PPO-LunarLander-v2/data

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         "__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 sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\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 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    ",
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         "__abstractmethods__": "frozenset()",
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     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
         ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
     },
-    "_n_updates": 248,
-    "n_steps": 1024,
+    "_n_updates": 1956,
+    "n_steps": 128,
     "gamma": 0.999,
     "gae_lambda": 0.98,
     "ent_coef": 0.01,
     "vf_coef": 0.5,
     "max_grad_norm": 0.5,
-    "batch_size": 64,
+    "batch_size": 128,
     "n_epochs": 4,
     "clip_range": {
         ":type:": "<class 'function'>",

PPO-LunarLander-v2/policy.optimizer.pth

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+oid sha256:47f876b317f3e88e7f2f549fdade0fac2d8f67a7f0b5cf9dcda7c57341f3ff12
+size 87545

PPO-LunarLander-v2/policy.pth

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 size 43073

PPO-LunarLander-v2/system_info.txt

@@ -1,7 +1,7 @@
-OS: macOS-12.3.1-arm64-arm-64bit Darwin Kernel Version 21.4.0: Fri Mar 18 00:47:26 PDT 2022; root:xnu-8020.101.4~15/RELEASE_ARM64_T8101
-Python: 3.9.12
-Stable-Baselines3: 1.5.0
-PyTorch: 1.11.0
+OS: macOS-13.0.1-arm64-arm-64bit Darwin Kernel Version 22.1.0: Sun Oct  9 20:14:30 PDT 2022; root:xnu-8792.41.9~2/RELEASE_ARM64_T8103
+Python: 3.9.13
+Stable-Baselines3: 1.6.0
+PyTorch: 1.13.0
 GPU Enabled: False
-Numpy: 1.22.3
+Numpy: 1.23.3
 Gym: 0.21.0

README.md

@@ -10,7 +10,7 @@ model-index:
   results:
   - metrics:
     - type: mean_reward
-      value: 252.25 +/- 20.02
+      value: 181.52 +/- 47.07
       name: mean_reward
     task:
       type: reinforcement-learning
@@ -20,9 +20,17 @@ model-index:
       type: LunarLander-v2
 ---
 
-  # **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
-  
+# **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

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If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\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 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 0x131578160>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x1315781f0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x131578280>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor 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results.json

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