Update README.md

README.md

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   # **ppo** Agent playing **Huggy**
-  This is a trained model of a **ppo** agent playing **Huggy**
-  using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
+  Modelo treinado de um agente **ppo** playing **Huggy**
+  utilizando a bibloteca [Unity ML-Agents](https://github.com/Unity-Technologies/ml-agents).
+
+  Treinamento do modelo se deu como parte da Residência em IA
 
   ## Usage (with ML-Agents)
-  The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
+  Documentação: 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
+  ### Assista o agente
   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: fabrycionakano/ppo-Huggy
-  3. Step 2: Select your *.nn /*.onnx file
-  4. Click on Watch the agent play 👀
+  1. Step 1: Ache o seu model_id. O meu se encontra no seguinte repositório: fabrycionakano/ppo-Huggy
+  2. Step 2: Selecione seu arquivo *.nn /*.onnx 
+  3. Acesse https://huggingface.co/spaces/ThomasSimonini/Huggy e clique em "Load your Huggy model"
+  4. Siga as instruções
+  5. Clique em Watch the agent play