4. Robust Multi-Agent Reinforcement Learning for Cooperative Lane Changing

Vehicles approaching highway merges or lane-changing situations need to coordinate their actions to maintain safety and traffic efficiency. This project investigates whether multiple reinforcement learning agents can learn cooperative lane-changing strategies. Different training approaches will be compared with conventional rule-based strategies. The project will evaluate their impact on traffic safety, travel time and overall traffic throughput, as well as their robustness under changing conditions.