As travel demand increases and urban traffic condition becomes more complicated, applying multi-agent deep reinforcement learning (MARL) to traffic signal control becomes one of the hot topics. The rise of Reinforcement Learning (RL) has opened up opportunities for solving Adaptive Traffic Signal Control (ATSC) in complex urban traffic networks, and deep neural networks have further enhanced their ability to handle complex data. Traditional research in traffic signal control is based on the centralized Reinforcement Learning technique. However, in a large-scale road network, centralized RL is infeasible because of an exponential growth of joint state-action space. In this paper, we propose a Friend-Deep Q-network (Friend-DQN) approach for multiple traffic signal control in urban networks, which is based on an agent-cooperation scheme. In particular, the cooperation between multiple agents can reduce the state-action space and thus speed up the convergence. We use SUMO (Simulation of Urban Transport) platform to evaluate the performance of Friend-DQN model, and show its feasibility and superiority over other existing methods.

翻译：随着出行需求的增加和城市交通状况的日益复杂，将多智能体深度强化学习应用于交通信号控制已成为热点课题之一。强化学习的兴起为解决复杂城市交通网络中的自适应交通信号控制开辟了机遇，而深度神经网络进一步增强了其处理复杂数据的能力。传统交通信号控制研究基于集中式强化学习技术。然而，在大规模道路网络中，由于联合状态-动作空间呈指数级增长，集中式强化学习并不可行。本文提出一种基于智能体协作机制的"朋友深度Q网络"方法，用于城市网络中的多交通信号控制。特别地，多智能体之间的协作能够缩减状态-动作空间，从而加速收敛。我们使用SUMO城市交通仿真平台评估Friend-DQN模型的性能，并展示了其相对于现有其他方法的可行性与优越性。