Learning Scalable Multi-Agent Coordination by Spatial Differential for Traffic Signal Control
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The intelligent control of the traffic signal is critical to the optimization of transportation systems. To achieve global optimal traffic efficiency in large-scale road networks, recent works have focused on coordination among intersections, which have shown promising results. However, existing studies paid more attention to the sensation sharing among intersections and did not care about the consequences after decisions. In this paper, we design a multi-agent coordination framework based on Deep Reinforcement Learning method for traffic signal control, defined as gamma-Reward that includes both original gamma-Reward and gamma-Attention-Reward. Specifically, we propose spatial differential method for coordination which uses the temporal-spatial reward information in the replay buffer to amend the reward of each action. A detailed theoretical analysis is given that proves the proposed model can converge to Nash equilibrium. By extending the idea of Markov Chain to the dimension of space-time, this coordination mechanism replaces the graph attention method and realizes the decoupling of the road network, which is scalable and more in line with practice. The simulation results show that the proposed model can get better performance than previous studies by amending the reward.
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