Reinforcement Learning for smart mobility

Reinforcement learning (RL) for smart mobility involves training intelligent systems to optimize transportation and mobility solutions through iterative learning and decision-making. By interacting with dynamic environments like traffic conditions and user preferences, RL algorithms enable vehicles and mobility systems to learn and adapt strategies to maximize efficiency, reduce congestion, and improve user experience. Our research group specializes in developing RL techniques tailored for smart mobility applications, including autonomous driving, traffic management, and ride-sharing optimization. Explore our publications to uncover cutting-edge advancements and innovative approaches shaping the future of intelligent transportation systems.