Update README.md

README.md

@@ -1,35 +1,12 @@
----
-library_name: ml-agents
-tags:
-- SoccerTwos
-- deep-reinforcement-learning
-- reinforcement-learning
-- ML-Agents-SoccerTwos
----
+
 
   # **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://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
-
-  We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
-  - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
-  browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
-  - A *longer tutorial* to understand how works ML-Agents:
-  https://huggingface.co/learn/deep-rl-course/unit5/introduction
-
   ### Resume the training
   ```bash
   mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
   ```
 
-  ### Watch your Agent play
-  You can watch your agent **playing directly in your browser**
-
-  1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
-  2. Step 1: Find your model_id: yoshq/poca-SoccerTwos
-  3. Step 2: Select your *.nn /*.onnx file
-  4. Click on Watch the agent play 👀