Mix-attention approximation for homogeneous large-scale multi-agent reinforcement learning-Reference-Cited by-同舟云学术

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Mix-attention approximation for homogeneous large-scale multi-agent reinforcement learning

Published:2022-10-07 Issue:4 Volume:35 Page:3143-3154
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Shike Yang,Jingchen Li,Haobin Shi^ORCID

Funder

National Natural Science Foundation of China

Open Research Projects of Zhejiang Lab

Shaanxi Province Key Research and Development Program of China

CAAI-Huawei MindSpore Open Fund

Doctor’s Scientific Research and Innovation Foundation of Northwestern Polytechnical University

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

https://link.springer.com/content/pdf/10.1007/s00521-022-07880-4.pdf

Reference40 articles.

1. Du W, Ding S (2020) A survey on multi-agent deep reinforcement learning: from the perspective of challenges and applications. Artif Intell Rev 54:3215–3238

2. Shi H, Li J, Mao J, Hwang K-S (2021) Lateral transfer learning for multiagent reinforcement learning. IEEE Trans Cybern (Early Access)

3. Zhang L, Li J, Shi H, Hwang K-S et al (2022) Multi-agent reinforcement learning by the actor-critic model with an attention interface. Neurocomputing 471:275–284

4. Busoniu L, Babuska R, De Schutter B (2006) Multi-agent reinforcement learning: a survey. In: 2006 9th international conference on control, automation, robotics and vision. IEEE, pp 1–6

5. Wei E, Luke S (2016) Lenient learning in independent-learner stochastic cooperative games. J Mach Learn Res 17(1):2914–2955

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