Research on Multi-Agent Reinforcement Learning Traffic Control

Author:

Fu Xinpeng1,Chen Simin1,Liang Qixian1,Li Yueqiao1

Affiliation:

1. University of Electronic Science and Technology of China Zhongshan Institute,Zhongshan,China

Publisher

IEEE

Reference15 articles.

1. Multiagent Reinforcement Learning for Integrated Network of Adaptive Traffic Signal Controllers (MARLIN-ATSC): Methodology and Large-Scale Application on Downtown Toronto

2. Coordinated deep reinforcement learners for traffic light control;van der pol;Proceedings of Learning Inference and Control of Multi-Agent Systems (at NIPS 2016),2016

3. STMARL: A Spatio-Temporal Multi-Agent Reinforcement Learning Approach for Cooperative Traffic Light Control[J];wang;IEEE Transactions on Mobile Computing,2020

4. Study on the influence of road capacity and information feedback on urban traffic system equilibrium state[J];xiaoyan;Physica A Statistical Mechanics and its Applications,2022

5. Reinforcement learning with average cost for adaptive cont;prashanth,2011

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