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Traffic lights play a vital role in the operation of the traffic network. However, with the rapid development of traffic, roads are becoming more and more complex, and vehicles are becoming more and more numerous, which leads to the increasing pressure of traffic lights scheduling, but the regulation ability is becoming weaker and weaker. In order to solve this problem, this paper established the convergence trace source (CTS) scheme. This scheme used the traffic light, the main object of traffic diversion, as an agent for reinforcement learning to optimize its ability to control traffic diversion. It comprehensively analyzed the congestion situation of the road network by constructing the congestion chain and congestion ring, and used the traffic light phase and its timing data to achieve the comprehensive judgment of the object state of the traffic light agent. This scheme designed the traffic light queue length algorithm, and used the digitization of congestion as an agent reward to evaluate the optimization effect. This paper used the SUMO simulation environment for experiments, designed and compared the average queue length at the intersection of the traffic optimization index. Finally, the average queue length at the intersection of this scheme is increased by 40% compared with the original data. [ABSTRACT FROM AUTHOR]

Focusing on the problem with traffic congestion under changing environmental conditions, this paper proposed a trajectory reward light (TR-light) model by combining reinforcement learning, neural network, multi-agent and traffic simulation technology to optimize the traffic at multi-intersections. This method had considerable merits in the following aspects. The traffic organization plan was formulated based on traffic lights; multi-agent reinforcement learning was used on traffic light control; regional traffic organization was optimized through the coordination of traffic lights; the agent implemented trajectory reconstruction after the execution of each behavior so as to change the vehicle travel path without changing the OD pair, and to calculate the final reward of the agent according to the plan and reconstructed trajectory. Finally, it conducted a traffic simulation experiment through SUMO. The comparison of traffic indicators verifies that the proposed model improves the smoothness of the road network and the traffic state at the multi-intersections. Experiments show that the model is feasible and effectively mitigates the traffic congestion. [ABSTRACT FROM AUTHOR]

At present, multi-agent reinforcement learning algorithms mostly adopt frameworks that are centralized in learning and decentralized in action. These frameworks may take too long to converge or may not converge at all. In order to speed up the collective learning time of multi-agents, this paper proposed a novel multi-agent group learning strategy. It used recurrent neural network (RNN) to predict the grouping matrix of multi-agents to share the experience between them, resulting in improved learning efficiency within the multi-agents group. Meanwhile, this paper proposed the concept of information trace to remedy the problem that the agents could not share information brought by the grouping. In order to strengthen the retention of excellent experience within the group, this paper proposed the practice of delaying the death time of excellent agents in the group. Finally, the results show that, compared to MADDPG, the training time is reduced by 12% in the labyrinth experiment and by 17 % in capture the flag experiment. [ABSTRACT FROM AUTHOR]