Push agent to the Hub

README.md

@@ -1,36 +1,61 @@
 ---
-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:
-  - metrics:
-    - type: mean_reward
-      value: 282.46 +/- 19.55
-      name: mean_reward
-    task:
+  - task:
       type: reinforcement-learning
       name: reinforcement-learning
     dataset:
       name: LunarLander-v2
       type: LunarLander-v2
+    metrics:
+    - type: mean_reward
+      value: 66.81 +/- 64.80
+      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).
-
-## Usage (with Stable-baselines3)
-TODO: Add your code
-
-
-```python
-from stable_baselines3 import ...
-from huggingface_sb3 import load_from_hub
+  # PPO Agent Playing LunarLander-v2
 
-...
-```
+  This is a trained model of a PPO agent playing LunarLander-v2.
+    
+  # Hyperparameters
+  ```python
+  {'exp_name': 'ppo'
+'seed': 1
+'torch_deterministic': True
+'cuda': True
+'track': False
+'wandb_project_name': 'cleanRL'
+'wandb_entity': None
+'capture_video': False
+'env_id': 'LunarLander-v2'
+'total_timesteps': 500000
+'learning_rate': 0.00025
+'num_envs': 4
+'num_steps': 128
+'anneal_lr': True
+'gae': True
+'gamma': 0.99
+'gae_lambda': 0.95
+'num_minibatches': 4
+'update_epochs': 4
+'norm_adv': True
+'clip_coef': 0.2
+'clip_vloss': True
+'ent_coef': 0.01
+'vf_coef': 0.5
+'max_grad_norm': 0.5
+'target_kl': None
+'repo_id': 'AntiSquid/PPO-LunarLander-v2'
+'batch_size': 512
+'minibatch_size': 128}
+  ```
+  

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model.pt

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results.json

@@ -1 +1 @@
-{"mean_reward": 282.45668832497824, "std_reward": 19.549892926397302, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-07-12T00:45:11.904791"}
+{"env_id": "LunarLander-v2", "mean_reward": 66.81432751624162, "std_reward": 64.79910087452345, "n_evaluation_episodes": 10, "eval_datetime": "2023-03-09T13:56:57.141695"}