Multi-Agent Reinforcement Learning Based on Representational Communication for Large-Scale Traffic Signal Control-Reference-Cited by-同舟云学术

Multi-Agent Reinforcement Learning Based on Representational Communication for Large-Scale Traffic Signal Control

Author:

Bokade Rohit¹^ORCID,Jin Xiaoning¹^ORCID,Amato Christopher²

Affiliation:

1. Department of Mechanical and Industrial Engineering, Northeastern University, Boston, USA

2. Khoury College of Computer Sciences, Northeastern University, Boston, USA

Funder

Northeastern University Tier 1 Award titled “Multi-Agent Reinforcement Learning Framework for Learning Coordination and Decision-Making

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

Reference78 articles.

1. Coordinated deep reinforcement learners for traffic light control;van der pol;Proc Learn Inference Control Multi-Agent Syst (NIPS),2016

2. The concrete distribution: A continuous relaxation of discrete random variables;maddison;arXiv 1611 00712,2016

3. Using a deep reinforcement learning agent for traffic signal control;genders;arXiv 1611 01142,2016

4. Categorical reparameterization with Gumbel–Softmax;jang;arXiv 1611 01144,2016

5. Deep reinforcement learning for traffic light control in vehicular networks;liang;arXiv 1803 11115,2018

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