Push agent to the Hub

README.md

@@ -1,12 +1,13 @@
 ---
-library_name: stable-baselines3
 tags:
 - LunarLander-v2
+- ppo
 - deep-reinforcement-learning
 - reinforcement-learning
-- stable-baselines3
+- custom-implementation
+- deep-rl-course
 model-index:
-- name: PPO-MLPPolicy
+- name: PPO
   results:
   - task:
       type: reinforcement-learning
@@ -16,22 +17,42 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 278.65 +/- 28.36
+      value: 116.92 +/- 41.03
       name: mean_reward
       verified: false
 ---
 
-# **PPO-MLPPolicy** Agent playing **LunarLander-v2**
-This is a trained model of a **PPO-MLPPolicy** 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
-
-
-```python
-from stable_baselines3 import ...
-from huggingface_sb3 import load_from_hub
-
-...
-```
+  This is a trained model of a PPO agent playing LunarLander-v2.
+    
+  # Hyperparameters
+  ```python
+  {'exp_name': 'exp1'
+'gym_id': 'LunarLander-v2'
+'learning_rate': 0.00025
+'seed': 1
+'total_timesteps': 50000
+'torch_deterministic': True
+'cuda': True
+'capture_video': True
+'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
+'batch_size': 512
+'minibatch_size': 128
+'env_id': 'LunarLander-v2'}
+  ```
+  

logs/events.out.tfevents.1677423335.185a80c4e60f.297.1

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+size 6357688

model.pt

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+oid sha256:0e44accdabba1a215c77675db132c845ec1619a50d0bc7103afeeaf20f095a60
+size 42469

results.json

@@ -1 +1 @@
-{"mean_reward": 278.6523560756656, "std_reward": 28.361914408874917, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-09T10:34:50.411569"}
+{"env_id": "LunarLander-v2", "mean_reward": 116.91909584583068, "std_reward": 41.02527324286631, "n_evaluation_episodes": 10, "eval_datetime": "2023-02-26T15:09:11.296623"}