A Survey on Population-Based Deep Reinforcement Learning

Many real-world applications can be described as large-scale games of imperfect information, which require extensive prior domain knowledge, especially in competitive or human–AI cooperation settings. Population-based training methods have become a popular solution to learn robust policies without a...

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Bibliographic Details
Published in:	Mathematics (Basel) Vol. 11; no. 10; p. 2234
Main Authors:	Long, Weifan, Hou, Taixian, Wei, Xiaoyi, Yan, Shichao, Zhai, Peng, Zhang, Lihua
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.05.2023
Subjects:	Algorithms Data mining Decision making Deep learning Digital libraries Equilibrium Game theory Logic programming Machine learning Mathematics multi-agent reinforcement learning Multiagent systems Policies population play reinforcement learning Robotics Robustness (mathematics) self play Surveys Training
ISSN:	2227-7390, 2227-7390
Online Access:	Get full text
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