A Novel Multi-Agent Deep RL Approach for Traffic Signal Control

TL;DR

This paper introduces Friend-DQN, a multi-agent deep reinforcement learning method for traffic signal control that improves scalability and efficiency in complex urban networks through agent cooperation.

Contribution

It proposes a novel Friend-DQN approach that leverages agent cooperation to reduce complexity and enhance performance in multi-agent traffic signal control.

Findings

Friend-DQN outperforms existing methods in simulation.

Cooperation among agents accelerates convergence.

The approach is feasible and effective in urban traffic scenarios.

Abstract

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.