TR-light：基于多信号灯强化学习的交通组织方案优化算法

针对多变环境条件下的交通堵塞问题，将强化学习、神经网络、多智能体和交通仿真技术结合起来，提出了用于优化多路口条件下交通状况的trajectory reward light(TR-light)模型。该方法具有几个显著特点：基于红绿灯拟定交通组织方案；将多智能体强化学习用于红绿灯控制；通过红绿灯的协同达到区域级的交通组织优化；在智能体每次行为执行结束后实施轨迹重构，在OD对不改变的情况下改变车辆行驶路径，根据方案和重构轨迹来计算智能体的最终回报。通过SUMO进行交通仿真实验和交通指标对比，验证了该模型在多交叉口中能够提高路网畅通率，改善交通状态。实验表明该模型可行，可有效缓解交通拥堵。

TR-light:traffic organization plan optimization algorithm based on multiple traffic signal lights reinforcement learning

Focusing on the problem with traffic congestion under changing environmental conditions, the 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.