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

Shijie Wang, Shangbo Wang
6/5/2023
cs.AIcs.MA

No official code found. Best implementation: cts198859/deeprl_signal_control (427 stars, MIT, last commit 2022-05-31).

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.

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Code Implementations(1)

cts198859/deeprl_signal_controlCommunity72%
MIT

multi-agent deep reinforcement learning for large-scale traffic signal control.

42790Shell, PythonDec 1, 20174 years agoMIT

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Cite this paper

@article{wang2023novel,
  title  = {A Novel Multi-Agent Deep RL Approach for Traffic Signal Control},
  author = {Shijie Wang and Shangbo Wang},
  year   = {2023},
  eprint = {2306.02684},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2306.02684v1}
}

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