High-speed, multi-agent autonomous vehicle racing demands agents that can make split-second decisions while strategically interacting with unpredictable opponents. This thesis will develop a learning-based racing agent and a model-based one both capable of long-term reasoning, balancing raw speed with tactical maneuvers, and benchmark their performance under head-to-head competition.
Requirements and tools
- Technical
- Vehicle Dynamics
- MPC
- MPPI
- Reinforcement Learning
- Software
- Python
- Nvidia Isaac Sim
- PyBullet
Contacts: Michael Khayyat, Stefano Arrigoni