Mixed Human-Autonomous Traffic Systems
Reinforcement learning and simulation for emergent behavior in traffic systems.
This line of work studies how autonomous agents and human drivers interact in mixed traffic environments, and how reinforcement learning can shape system-level driving behavior.
The broader goal is to understand emergent behavior in adaptive multi-agent systems, especially when learned policies interact with human-like behavior.