META: A City-Wide Taxi Repositioning Framework Based on Multi-Agent Reinforcement Learning

The popularity of online ride-hailing platforms has made people travel smarter than ever before. But people still frequently encounter the dilemma of " taxi drivers hunt passengers and passengers search for unoccupied taxis ". Many studies try to reposition idle taxis to alleviate such issues by using reinforcement learning based methods, as they are capable of capturing future demand/supply dynamics. However, they either coordinate all city-wide taxis in a centralized manner or treat all taxis in a region homogeneously, resulting in inefficient or inaccurate learning performance. In this paper, we propose a multi-agent reinforcement learning based framework named META ( M ak E T axi A ct differently in each agent) to mitigate the disequilibrium of supply and demand via repositioning taxis at the city scale. We decompose it into two subproblems, i.e., taxi demand/supply determination and taxi dispatching strategy formulation. Two components are wisely built in META to address the gap collaboratively, in which each region is regarded as an agent and taxis inside the region can make two different actions. Extensive experiments demonstrate that META outperforms existing methods.