Zobrazeno 1 - 10
of 71
pro vyhledávání: '"Li Pengyi"'
Leveraging the powerful generative capability of diffusion models (DMs) to build decision-making agents has achieved extensive success. However, there is still a demand for an easy-to-use and modularized open-source library that offers customized and
Externí odkaz:
http://arxiv.org/abs/2406.09509
Diffusion planning has been recognized as an effective decision-making paradigm in various domains. The capability of generating high-quality long-horizon trajectories makes it a promising research direction. However, existing diffusion planning meth
Externí odkaz:
http://arxiv.org/abs/2401.15443
Evolutionary Reinforcement Learning (ERL), which integrates Evolutionary Algorithms (EAs) and Reinforcement Learning (RL) for optimization, has demonstrated remarkable performance advancements. By fusing both approaches, ERL has emerged as a promisin
Externí odkaz:
http://arxiv.org/abs/2401.11963
Deep Reinforcement Learning (Deep RL) and Evolutionary Algorithms (EA) are two major paradigms of policy optimization with distinct learning principles, i.e., gradient-based v.s. gradient-free. An appealing research direction is integrating Deep RL a
Externí odkaz:
http://arxiv.org/abs/2210.17375
PMIC: Improving Multi-Agent Reinforcement Learning with Progressive Mutual Information Collaboration
Autor:
Li, Pengyi, Tang, Hongyao, Yang, Tianpei, Hao, Xiaotian, Sang, Tong, Zheng, Yan, Hao, Jianye, Taylor, Matthew E., Tao, Wenyuan, Wang, Zhen, Barez, Fazl
Learning to collaborate is critical in Multi-Agent Reinforcement Learning (MARL). Previous works promote collaboration by maximizing the correlation of agents' behaviors, which is typically characterized by Mutual Information (MI) in different forms.
Externí odkaz:
http://arxiv.org/abs/2203.08553
Autor:
Li, Boyan, Tang, Hongyao, Zheng, Yan, Hao, Jianye, Li, Pengyi, Wang, Zhen, Meng, Zhaopeng, Wang, Li
Discrete-continuous hybrid action space is a natural setting in many practical problems, such as robot control and game AI. However, most previous Reinforcement Learning (RL) works only demonstrate the success in controlling with either discrete or c
Externí odkaz:
http://arxiv.org/abs/2109.05490
Autor:
Chen, Nuo, Gao, Pei, Jiang, Qixing, Yu, Xiaojuan, Li, Pengyi, Xu, Yanshun, Yu, Dawei, Yang, Fang, Xia, Wenshui
Publikováno v:
In Food Research International October 2022 160
Autor:
Yang, Wenfeng1 (AUTHOR) st098651@student.spbu.ru, Li, Pengyi1 (AUTHOR), Yang, Wei1 (AUTHOR), Liu, Yuxing1 (AUTHOR), He, Yulong1 (AUTHOR), Petrosian, Ovanes1 (AUTHOR), Davydenko, Aleksandr1 (AUTHOR)
Publikováno v:
Mathematics (2227-7390). Apr2023, Vol. 11 Issue 7, p1733. 16p.
Akademický článek
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Autor:
Liu, Peirong1 (AUTHOR), Li, Pengyi1 (AUTHOR), Li, Qingyang2 (AUTHOR), Yan, Hongzhu1 (AUTHOR), Shi, Xiaowei1 (AUTHOR), Liu, Chunliang1 (AUTHOR), Zhang, Yu1 (AUTHOR), Peng, Sheng1 (AUTHOR)
Publikováno v:
Journal of Investigative Surgery. Aug2021, Vol. 34 Issue 8, p883-888. 6p.