Hybrid cooperative agents with online reinforcement learning for traffic control

This paper presents the application of fuzzy-neuroevolutionary hybrid system with online reinforcement learning for intelligent road traffic management and control. Taking a step away from the conventional traffic control system, the hybrid system presents different methodologies in knowledge acquisition, decision-making, learning and goal formulation with the use of a three-layered hierarchical, distributed agent architecture. Distributed and hierarchical fuzzy knowledge acquisition allows different levels of perception to be derived for the same traffic situation by the intelligent agents. Agents' perceptions can be changed with the use of online reinforcement learning. Initial experimental results show that the implementation of the hybrid agents in the traffic network generally yields better network performance when compared to a network without the agents. The probability of a traffic network evolving into pathological states with oversaturation is also reduced with the implementation of the agents.