Can Bayesian RL improve multi-intersection traffic signal control?
Bayesian Critique-Tune-Based Reinforcement Learning with Attention-Based Adaptive Pressure for Multi-Intersection Traffic Signal Control
This paper introduces BCT-APRL, a new method for controlling traffic signals at multiple intersections using reinforcement learning (RL). It aims to improve traffic flow by using a two-layer Bayesian system (Critique-Tune) to make better RL decisions and a new way to represent traffic states (Attention-Based Adaptive Pressure) that considers the impact of different lanes on traffic flow.
The Critique-Tune framework helps the RL agent make better decisions about traffic signal timing by evaluating and refining the RL agent's policies, pushing them toward a globally optimal solution rather than getting stuck in a suboptimal one. The attention-based adaptive pressure component allows the system to represent complex traffic situations more effectively by weighting the importance of different lanes, improving responsiveness to dynamic traffic changes. These are particularly relevant to LLM-based multi-agent systems because they improve decision making and state representation, which are key challenges in complex, real-time environments like traffic management where multiple agents (intersections) interact.