Quick Start Guide

This guide will help you get started with RLRoverLab quickly. Follow these steps to set up the environment and run your first training or evaluation.

Prerequisites

Before starting, ensure you have:

• NVIDIA GPU with at least 8GB VRAM
• Ubuntu 20.04 or 22.04
• Docker and NVIDIA Container Toolkit installed (see Docker Installation)

Quick Setup with Docker

1. Clone the repository:

   git clone https://github.com/abmoRobotics/RLRoverLab
   cd RLRoverLab
   

2. Download terrain assets:

   pip3 install gdown
   python3 download_usd.py
   

3. Start the Docker container:

   cd docker
   ./run.sh
   docker exec -it rover-lab-base bash
   

Running Your First Example

1. Train a Simple Agent

Train a PPO agent on the simple AAU rover environment:

cd examples/02_training
/workspace/isaac_lab/isaaclab.sh -p train.py --task="AAURoverEnvSimple-v0" --num_envs=128

2. Evaluate a Pre-trained Model

If you have a trained model, evaluate it:

cd examples/03_inference
/workspace/isaac_lab/isaaclab.sh -p eval.py --task="AAURoverEnvSimple-v0" --num_envs=32 --checkpoint=path/to/your/model.pt

3. Demo with Zero Agent

Run a basic demo to see the environment:

cd examples/01_demos
/workspace/isaac_lab/isaaclab.sh -p 01_zero_agent.py

Available Environments

The suite provides several pre-configured environments:

What's Next?

• Explore more examples
• Learn about available environments
• Understand the training process
• Add your own robot

Troubleshooting

Common Issues

1. GPU Memory Issues: Reduce --num_envs parameter
2. Docker Permission Issues: Ensure your user is in the docker group
3. Display Issues: Run xhost +local:docker before starting the container

For more detailed troubleshooting, see the Installation Guide.