Improving traffic signal control effect of multiagent reinforcement learning with information collaboration

In order to study the parameter selection of the information collaboration, this paper analyzes the model control effect and model complexity under these two kinds of information synergy representations by constructing the traffic state-aware interaction and reward information synergy experiments, and finally selects the optimal model information synergy parameters according to the experimental results, which provides a certain research basis for the subsequent parameter settings.