关键词: 智能交通, 分布式交通信号控制, 多智能体强化学习, 不均衡需求下的城市道路网络, 博弈论, 数值模拟

Abstract:

The difficulty of distributed signal control is increasing due to the unbalance and fluctuation of traffic demand. Since the decision-making of existing independent action multi-agent reinforcement learning (IA-MARL) is based on its own historical experience, the distributed signal control based on IA-MARL is difficult to timely alleviate the impact of unbalanced and fluctuating traffic demand. In this paper, the framework of multi- agent reinforcement learning based on the game (G-MARL) was proposed by improving the decision- making of IAMARL with integrating the mixed strategy Nash- equilibrium, which is a concept in game theory. In the grid network with the Poisson arrival rate, the distributed control methods based on IA-MARL and G-MARL were simulated to obtain the unit travel time and the unit vehicle delay curves. The results show that, the unit travel time and the unit vehicle average delay obtained by G-MARL are reduced by 59.94% and 81.45% compared with IAMARL respectively. It is proved that G-MARL is suitable for distributed signal control when there are unbalances and fluctuations in traffic demand with the unsaturated state.

Key words: intelligent transportation, distributed traffic signal control, multi-agent reinforcement learning, urban road network under unbalanced demand, game theory, numerical simulation