Push PPO agent to Hub

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+replay.mp4 filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,10 +1,11 @@
 ---
-library_name: stable-baselines3
 tags:
 - LunarLander-v2
+- ppo
 - deep-reinforcement-learning
 - reinforcement-learning
-- stable-baselines3
+- custom-implementation
+- deep-rl-course
 model-index:
 - name: PPO
   results:
@@ -16,22 +17,19 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 244.67 +/- 17.26
+      value: 13.46 +/- 125.62
       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).
+# PPO Agent Playing LunarLander-v2
 
-## Usage (with Stable-baselines3)
-TODO: Add your code
+This is a trained model of a PPO agent playing LunarLander-v2.
 
+## Mean Reward
+13.46 +/- 125.62
 
-```python
-from stable_baselines3 import ...
-from huggingface_sb3 import load_from_hub
-
-...
+## Hyperparameters
+```
+{'env_id': 'LunarLander-v2', 'repo_id': 'Tass-k/ppo-LunarLander-v2'}
 ```

model.pt

@@ -0,0 +1,3 @@
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+size 43419

replay.mp4

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+oid sha256:bb9e06f7ebb2793e641ddd7ce854c00c74820ed0e4beb1e0c655fcd40a12cd52
+size 142674

results.json

@@ -1 +1 @@
-{"mean_reward": 244.6706236, "std_reward": 17.257353265949526, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2026-03-15T12:11:33.242110"}
+{"env_id": "LunarLander-v2", "mean_reward": 13.459144633334512, "std_reward": 125.62018784459613, "n_evaluation_episodes": 10, "eval_datetime": "2026-03-25T16:55:32.342361"}