Switching Policies based on Multi-Objective Reinforcement Learning for Adaptive Traffic Signal Control

Reinforcement learning can be applied to signal control to achieve efficient control while reducing the cost of manual configuration. However, as reinforcement learning acquires control that is specific to the environment at the time of learning, conventional methods assume that the optimal control is the same at the time of learning and application, and that it remains consistent despite changes in traffic flow. In this study, we address this problem using multi-objective reinforcement learning, in which multiple policies are obtained by changing the weights. We propose a method to obtain control laws exhaustively for various traffic flow ratios and switch between them when applied, using the traffic flow ratios as weights and the rewards for each road as rewards for each objective. The superiority of the proposed method is verified by two computer experiments, one with a single agent and the other with multiple agents.