CVAE-based Far-sighted Intention Inference for Opponent Modeling in Multi-agent Reinforcement Learning

Most interactive environments are non-stationary for agents, as the behaviors of their opponents continually change, which can impair the performance of reinforcement learning algorithms. This impairment can be alleviated by modeling opponents to predict their future movements. To predict more preci...

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Vydáno v:	Chinese Control Conference s. 5847 - 5851
Hlavní autoři:	Pei, Yu, Xu, XiaoPeng, Liu, Zhong, Wang, Kuo, Zhu, Li, Wang, Dong
Médium:	Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	Technical Committee on Control Theory, Chinese Association of Automation 28.07.2024
Témata:	Analytical models Attention mechanisms conditional variational autoencoder (CVAE) Decision making Games intention inference Multi-agent reinforcement learning opponent modeling Prediction algorithms Predictive models Reinforcement learning
ISSN:	1934-1768
On-line přístup:	Získat plný text
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