Better value estimation in Q-learning-based multi-agent reinforcement learning-Reference-Cited by-同舟云学术

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Better value estimation in Q-learning-based multi-agent reinforcement learning

Published:2023-11-02 Issue: Volume: Page:
ISSN:1432-7643
Container-title:Soft Computing
language:en
Short-container-title:Soft Comput

Author:

Ding Ling,Du Wei,Zhang Jian,Guo Lili,Zhang Chenglong,Jin Di,Ding Shifei

Funder

the National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Geometry and Topology,Theoretical Computer Science,Software

Link

https://link.springer.com/content/pdf/10.1007/s00500-023-09365-5.pdf

Reference34 articles.

1. Carta S, Ferreira A, Podda AS, Recupero DR, Sanna A (2021) Multi-DQN: an ensemble of deep Q-learning agents for stock market forecasting. Expert Syst Appl 164:113820. https://doi.org/10.1016/j.eswa.2020.113820

2. Chen X, Xiong G, Lv Y, Chen Y, Song B & Wang FY (2021) A collaborative communication-Qmix approach for large-scale networked traffic signal control. In: 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), pp 3450–3455. https://doi.org/10.1109/TITS.2019.2901791

3. Chu T, Wang J, Codecà L, Li Z (2019) Multi-agent deep reinforcement learning for large-scale traffic signal control. IEEE Trans Intell Transp Syst 21(3):1086–1095. https://doi.org/10.1109/TITS.2019.2901791

4. Du W, Ding S, Guo L, Zhang J, Zhang C, Ding L (2022) Value function factorization with dynamic weighting for deep multi-agent reinforcement learning. Inf Sci 615:191–208. https://doi.org/10.1016/j.ins.2022.10.042

5. Foerster J, Nardelli N, Farquhar G, Afouras T, Torr PH, Kohli P & Whiteson S (2017) Stabilising experience replay for deep multi-agent reinforcement learning. In: International conference on machine learning, pp 1146–1155. https://doi.org/10.48550/arXiv.1702.08887

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