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README.md

@@ -1,30 +1,61 @@
 ---
 tags:
-- unity-ml-agents
-- ml-agents
+- LunarLander-v2
+- ppo
 - deep-reinforcement-learning
 - reinforcement-learning
-- ML-Agents-SoccerTwos
-library_name: ml-agents
+- custom-implementation
+- deep-rl-course
+model-index:
+- name: PPO
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+    metrics:
+    - type: mean_reward
+      value: -113.57 +/- 74.63
+      name: mean_reward
+      verified: false
 ---
-    
-  # **poca** Agent playing **SoccerTwos**
-  This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
-  
-  ## Usage (with ML-Agents)
-  The Documentation: https://github.com/huggingface/ml-agents#get-started
-  We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
 
+  # PPO Agent Playing LunarLander-v2
 
-  ### Resume the training
-  ```
-  mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
+  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': 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': 'kostasang/customPPO-LunarLander-v2'
+'batch_size': 512
+'minibatch_size': 128}
   ```
-  ### Watch your Agent play
-  You can watch your agent **playing directly in your browser:**.
-  
-  1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
-  2. Step 1: Write your model_id: kostasang/poca-SoccerTwos
-  3. Step 2: Select your *.nn /*.onnx file
-  4. Click on Watch the agent play 👀