Engineering A Large-Scale Traffic Signal Control: A Multi-Agent Reinforcement Learning Approach

Abstract

Reinforcement learning is of vital significance in machine learning and is also a promising approach for traffic signal control in urban road networks with assistance of deep neural networks. However, in a large scale urban network, the centralized reinforcement learning approach is beset with difficulties due to the extremely high dimension of joint action space. The multi-agent reinforcement learning (MARL) approach overcomes the high dimension problem by employing distributed local agents whose action space is much smaller. Even though, MARL approach introduces another issue that multiple agents interact with environment simultaneously causing its instability so that training each agent independently may not converge. This paper presents an actor-critic based decentralized MARL approach to control traffic signal which overcomes the shortcomings of both centralized RL approach and independent MARL approach. In particular, a distributed critic network is designed which overcomes the difficulty to train a large-scale neural network in centralized RL approach. Moreover, a difference reward method is proposed to evaluate the contribution of each agent, which accelerates the convergence of algorithm and makes agents optimize policy in a more accurate direction. The proposed MARL approach is compared against the fully independent approach and the centralized learning approach in a grid network. Simulation results demonstrate its effectiveness in terms of average travel speed, travel delay and queue length over other MARL algorithms.