Push agent to the Hub

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 +17,45 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 272.11 +/- 19.43
+      value: -147.07 +/- 80.80
       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).
+  # 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.
 
-
-```python
-from stable_baselines3 import ...
-from huggingface_sb3 import load_from_hub
-
-...
-```
+  # Hyperparameters
+  ```python
+  {'exp_name': 'unit8_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': 50000
+'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': '0xid/ppo-LunarLander-v2'
+'batch_size': 512
+'minibatch_size': 128}
+  ```
+  

logs/events.out.tfevents.1678315331.ilan-kingdom.22618.0

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+size 108487

model.pt

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+oid sha256:e440c9be5fde080894c6110ecbfbbec6b95f035d24c3f97c2e279afd99504652
+size 42597

results.json

@@ -1 +1 @@
-{"mean_reward": 272.1120885504889, "std_reward": 19.427457232038414, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-19T13:26:55.300431"}
+{"env_id": "LunarLander-v2", "mean_reward": -147.0728282779005, "std_reward": 80.80490431309705, "n_evaluation_episodes": 10, "eval_datetime": "2023-03-08T23:44:48.194696"}