Swarm Reinforcement Learning for traffic signal control based on cooperative multi-agent framework

Congestion, accidents, pollution, and many other problems resulting from urban traffic are present every day in most cities around the world. The growing number of traffic lights in intersections needs efficient control, and hence, automatic systems are essential nowadays for optimally tackling this task. Agent based technologies and reinforcements learning are largely used for modelling and controlling intelligent transportation systems, where agents represent a traffic signal controller. Each agent learns to achieve its goal through many episodes. With a complicated learning problem, it may take much computation time to acquire the optimal policy. In this paper, we use a population based methods such as particle swarm optimization to be able to find rapidly the global optimal solution for multi-modal functions with wide solution space. Agents learn through not only on their respective experiences, but also by exchanging information among them, simulation results show that the swarm Q-learning surpass the simple Q-learning causing less average delay time and higher flow rate.