Constrained Multi-Agent Reinforcement Learning for Managing Electric Self-Driving Taxis

Zhaoxing Yang, Guiyun Fan, Haiming Jin

Abstract: Electric self-driving taxis (es-taxis) draw great attention nowadays and hold the promise for future transportation due to their convenient and environment-friendly nature. However efficiently managing large-scale es-taxis remains an open problem. In this paper, we focus on scheduling es-taxis under charging budget constraint. Specifically, we design safe-controller to guarantee the satisfaction of budget constraint, and propose HAT framework to enlarge the sight for decision-making on deactivating es-taxis. As for the non-stationary induced by HAT, we analyze and limit its influence with theoretical guarantees. The overall framework Safe-HAT achieves superior performance in real-world data against other strong baselines.