Flexible Traffic Signal Control via Multi-Objective Reinforcement Learning

Deep reinforcement learning has been extensively studied for traffic signal control owing to its ability to process large amounts of information and achieving superior performance control. However, this method acquires flow-specific policies during learning. Thus, its performance under inexperienced traffic flows is not guaranteed. Moreover, the traffic signal control problem formulation assumes that the optimal policy differs for each traffic flow ratio owing to the trade-off between orthogonal roads at an intersection. Therefore, multiple policies must be switched to avoid performance decay for traffic flow changes. In this study, we use multi-objective reinforcement learning to determine the policy corresponding to each traffic flow ratio exhaustively. Subsequently, these policies are switched to the current traffic flow ratio to achieve flexible control over traffic flow changes. The proposed method achieves the shortest average travel times in all environments compared with rule-based and single-objective reinforcement learning methods for stationary traffic and traffic flows with varying flow ratios.