Upload folder using huggingface_hub

README.md

@@ -1,24 +1,14 @@
 
-    
-    ---
-    tags:
-    - CartPole-v1
-    - q-learning
-    - reinforcement-learning
-    - custom-implementation
-    metrics:
-    - type: mean_reward
-      value: 172.93 +/- 49.55
-    library_name: stable-baselines3
-    ---
-    
-
     # **DQN** Agent playing **CartPole-v1**
     This is a trained model of a **DQN** agent playing **CartPole-v1**.
-
+    
     ## Usage
     ```python
     from stable_baselines3 import DQN
     model = DQN.load("dqn_cartpole", env=env)
     ```
+
+    ## Evaluation Results
+    - Mean Reward: 165.61
+    - Standard Deviation: 36.78
     

dqn_cartpole.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:24c260ea7de7818b9bbabbc8d147f8fc6b2bfb70a6852db3cd3ee5be12256c5f
+oid sha256:f36fcf151eb821b4286ed8f9c31076a45362e72f91b1b691f4a36b081ba38548
 size 100110

metadata.yaml

@@ -0,0 +1,11 @@
+dataset:
+  name: CartPole-v1
+  type: gymnasium
+license: mit
+metrics:
+  mean_reward: 165.61 +/- 36.78
+tags:
+- CartPole-v1
+- q-learning
+- reinforcement-learning
+- custom-implementation

results.json

@@ -1 +1 @@
-{"env_id": "CartPole-v1", "mean_reward": 172.93, "std_reward": 49.54861350229691, "eval_datetime": "2024-12-18T22:12:43.310127"}
+{"env_id": "CartPole-v1", "mean_reward": 165.61, "std_reward": 36.77577871371319, "eval_datetime": "2024-12-18T22:15:25.471841"}