DQN LunarLander-v2 1M steps

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.gitattributes +1 -0
README.md +36 -0
config.json +1 -0
ppo-LinarLander-v2.zip +3 -0
ppo-LinarLander-v2/_stable_baselines3_version +1 -0
ppo-LinarLander-v2/data +115 -0
ppo-LinarLander-v2/policy.optimizer.pth +3 -0
ppo-LinarLander-v2/policy.pth +3 -0
ppo-LinarLander-v2/pytorch_variables.pth +3 -0
ppo-LinarLander-v2/system_info.txt +7 -0
replay.mp4 +3 -0
results.json +1 -0

.gitattributes CHANGED Viewed

@@ -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

 *.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

README.md ADDED Viewed

	@@ -0,0 +1,36 @@

+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: DQN
+  results:
+  - metrics:
+    - type: mean_reward
+      value: -46.76 +/- 20.50
+      name: mean_reward
+    task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+---
+# **DQN** Agent playing **LunarLander-v2**
+This is a trained model of a **DQN** 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 ADDED Viewed

	@@ -0,0 +1 @@

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+        "dtype": "float32",
+        "_shape": [
+            8
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+        "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
+        "high": "[inf inf inf inf inf inf inf inf]",
+        "bounded_below": "[False False False False False False False False]",
+        "bounded_above": "[False False False False False False False False]",
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1	+ {"mean_reward": -46.76247849020292, "std_reward": 20.49805161392167, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-07-03T23:17:03.577657"}