Flexible Traffic Signal Control via Multi-Objective Reinforcement Learning-Reference-Cited by-同舟云学术

Flexible Traffic Signal Control via Multi-Objective Reinforcement Learning

Published:2023 Issue: Volume:11 Page:75875-75883
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Saiki Takumi¹^ORCID,Arai Sachiyo¹^ORCID

Affiliation:

1. Department of Urban Environment Systems, Division of Earth and Environmental Sciences, Graduate School of Science and Engineering, Chiba University, Chiba, Japan

Funder

Industrial Technology Research Grant Program from the New Energy and Industrial Technology Development Organization (NEDO) of Japan

Japan Science and Technology Agency Support for Pioneering Research Initiated by the Next Generation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

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

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10185540.pdf?arnumber=10185540

Reference23 articles.

1. Distributed prioritized experience replay;horgan;arXiv 1803 00933,2018

2. Playing Atari with deep reinforcement learning;mnih;arXiv 1312 5602,2013

3. Microscopic Traffic Simulation using SUMO

4. Adaptive traffic signal control: Deep reinforcement learning algorithm with experience replay and target network;gao;arXiv 1705 02755,2017

5. An open-source framework for adaptive traffic signal control;genders;arXiv 1909 00395,2019

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