Update README.md

README.md

@@ -7,29 +7,103 @@ tags:
 - 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: Pulting86/ML-Agents-SoccerTwos
-  3. Step 2: Select your *.nn /*.onnx file
-  4. Click on Watch the agent play 👀
+# ML-Agents SoccerTwos Training
+
+Este repositorio contiene los pasos necesarios para entrenar un agente en el entorno `SoccerTwos` utilizando Unity ML-Agents.
+
+## Requisitos
+
+- Python 3.7 o superior
+- Unity ML-Agents
+- TensorFlow
+- Acceso al entorno `SoccerTwos` (archivo `.zip` descargable)
+
+## Instalación
+
+1. **Clona el repositorio de ML-Agents:**
+
+   ```bash
+   git clone --depth 1 https://github.com/Unity-Technologies/ml-agents
+
+2. **Instala los paquetes necesarios:**
+
+    Dirígete a la carpeta clonada y ejecuta:
+    ```bash
+    cd ml-agents
+    pip3 install -e ./ml-agents-envs
+    pip3 install -e ./ml-agents
+
+## Configuración del entorno de entrenamiento
+
+1. **Crea la carpeta para el entrenamiento:**
+    ```bash
+    mkdir -p ./train-soccer
+
+2. **Descarga el entorno SoccerTwos:**
+
+    Utiliza el siguiente script para descargar el archivo .zip del entorno y extraerlo:
+
+      ```bash
+      import gdown
+      file_id = '1KuqBKYiXiICU4kNMqEzhgyuPF5_45CL'
+      gdown.download(f'https://drive.google.com/uc?export=download&id={file_id}', './train-soccer/SoccerTwos.zip', quiet=False)
+  Extrae el archivo:
+  
+      unzip -d ./train-soccer ./train-soccer/SoccerTwos.zip
+
+3. **Cambia los permisos del archivo extraído:**
+
+    ```bash
+    chmod -R 755 ./train-soccer/SoccerTwos/SoccerTwos.x86_64
+
+
+
+## Configuración de parámetros de entrenamiento
+
+  Crea un archivo YAML con los parámetros de entrenamiento. Un ejemplo es el siguiente:
+
+    
+    behaviors:
+      SoccerTwos:
+        trainer_type: poca
+        hyperparameters:
+          batch_size: 4096
+          buffer_size: 40960
+          learning_rate: 0.0002
+          beta: 0.01
+          epsilon: 0.15
+          lambd: 0.92
+          num_epoch: 5
+          learning_rate_schedule: linear
+        network_settings:
+          normalize: true
+          hidden_units: 1024
+          num_layers: 3
+          vis_encode_type: simple
+        reward_signals:
+          extrinsic:
+            gamma: 0.995
+            strength: 1.0
+        keep_checkpoints: 10
+        max_steps: 1000000
+        time_horizon: 1200
+        summary_freq: 5000
+        self_play:
+          save_steps: 50000
+          team_change: 300000
+          swap_steps: 3000
+          play_against_latest_model_ratio: 0.6
+          initial_elo: 1200.0
+
+  Y guardarlo en la carpeta 'train-soccer'
+
+    with open('./train-soccer/SoccerTwos.yaml', 'w') as file:
+      file.write(yaml_content)
+
+
+## Entrenamiento del agente
+
+  Inicia el entrenamiento con el siguiente comando:
+
+    mlagents-learn ./train-soccer/SoccerTwos.yaml --env=./train-soccer/SoccerTwos/SoccerTwos.x86_64 --run-id="SoccerTwosNew" --no-graphics --force