Upload test training of LunarLander-v2 using PPO to jabot/PPO_LunarLanderV2

.gitattributes

@@ -25,3 +25,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.mp4 filter=lfs diff=lfs merge=lfs -text

PPO_LunarLanderV2_8000000Steps.zip

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PPO_LunarLanderV2_8000000Steps/_stable_baselines3_version

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PPO_LunarLanderV2_8000000Steps/data

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PPO_LunarLanderV2_8000000Steps/policy.optimizer.pth

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PPO_LunarLanderV2_8000000Steps/policy.pth

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PPO_LunarLanderV2_8000000Steps/pytorch_variables.pth

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PPO_LunarLanderV2_8000000Steps/system_info.txt

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+OS: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022
+Python: 3.7.13
+Stable-Baselines3: 1.5.0
+PyTorch: 1.11.0+cu113
+GPU Enabled: True
+Numpy: 1.21.6
+Gym: 0.21.0

README.md

@@ -0,0 +1,28 @@
+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - metrics:
+    - type: mean_reward
+      value: 283.01 +/- 16.05
+      name: mean_reward
+    task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      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
+  

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 0x7fb972859830>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fb9728598c0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fb972859950>", "_build_mlp_extractor": "<function 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replay.mp4

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+oid sha256:9bbd71af7e8a9844e8a6c72d69f7b219425c309da4e533fa46e38593fcc7d1c7
+size 197096

results.json

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+{"mean_reward": 283.0107404986814, "std_reward": 16.052435786016552, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-12T09:50:46.255630"}