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docs/source/en/user_guide/getting_started/hello_motrixlab.md

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+# Quick Start: Hello MotrixLab
+
+This tutorial demonstrates the MotrixLab workflow through a simple example - loading and training a cartpole environment:
+
+## Environment Preview
+
+We provide a simple script to visualize an environment without executing any training. This helps you verify that system dependencies are correctly configured:
+
+```bash
+uv run scripts/view.py --env cartpole
+```
+
+This will open a visualization window showing the cartpole physics simulation environment with random actions for demonstration.
+
+## Train Model
+
+Start training the cartpole balancing task:
+
+```bash
+uv run scripts/train.py --env cartpole
+```
+
+The training process will automatically:
+
+1. Automatically select training backend (JAX or PyTorch) based on hardware environment
+2. Create training environments
+3. Start PPO algorithm training
+
+Training results will be saved in the `runs/cartpole/` directory, including:
+
+-   Training checkpoints
+-   TensorBoard log files
+
+## Visualize Training Process
+
+If you want to observe the model's learning process during training, you can enable visualization rendering:
+
+```bash
+uv run scripts/train.py --env cartpole --render
+```
+
+### 🎮 Interactive Rendering Control
+
+> **Important Note**: Visualization significantly reduces training speed and is recommended mainly for debugging and demonstration purposes.
+
+During visualized training, you can use the **spacebar** to dynamically control rendering:
+
+-   **Enable Rendering**: Press spacebar to enable visualization and observe robot behavior
+-   **Disable Rendering**: Press spacebar again to disable rendering and improve training speed
+-   **Switch Anytime**: No need to restart the program; you can switch at any time during training
+
+This interactive control allows you to observe training effects when needed and enjoy fast training when not needed. This feature also works during inference.
+
+## View Training Results
+
+Use TensorBoard to view training progress:
+
+```bash
+uv run tensorboard --logdir runs/cartpole
+```
+
+## Test Trained Model
+
+After training is complete, test the trained policy:
+
+```bash
+# Automatically find best policy for testing (recommended)
+uv run scripts/play.py --env cartpole
+
+# Manually specify policy file for testing (if you need a specific version)
+uv run scripts/play.py --env cartpole --policy runs/cartpole/YOUR_RESULT_NUMBER/best_agent.pickle
+```
+
+> **Tip**: The system will automatically find the latest and best policy files in the `runs/cartpole/` directory. Usually, using the auto-discovery feature is sufficient.
+
+## That Completes Our Example
+
+Next, you can try modifying parameters to observe physical effects under different settings, or try other environments.
+
+## Next Steps
+
+-   Learn about the [Basic Framework](../tutorial/basic_frame.md)
+-   Study [Physics Environment Configuration](../tutorial/physics_environment.md)
+-   View more [Training Examples](../demo/cartpole.md)