Results 31 to 40 of about 10,350,910 (281)

Intent-Aware Multi-Agent Reinforcement Learning [PDF]

2018 IEEE International Conference on Robotics and Automation (ICRA), 2018
ICRA ...
Siyuan Qi, Song-Chun Zhu
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Review of Research on Agent Training Methods Toward Human-Agent Collaboration [PDF]

Jisuanji kexue
Human-agent collaboration has received widespread attention in recent years,and multi-agent reinforcement learning has demonstrated significant advantages and application potential in the field of human-agent collaboration.This paper first introduces the
HUANG Weiye, CHEN Xiliang, LAI Jun
doaj   +1 more source

Survey on multi-agent reinforcement learning methods from the perspective of population

智能科学与技术学报, 2023
Multi-agent systems are a cutting-edge research concept in the field of distributed artificial intelligence. Traditional multi-agent reinforcement learning methods mainly focus on topics such as group behavior emergence, multi-agent cooperation and ...
XIANG Fengtao, LUO Junren, GU Xueqiang, SU Jiongming, ZHANG Wanpeng   +4 more
doaj  

Multi-domain intelligent action planning method based on joint Evolutionary and reinforcement learning algorithm [PDF]

Zhihui kongzhi yu fangzhen
Aimed at intelligent planning for joint multi-domain operations, this study proposes an evolutionary strategy-reinforcement learning joint algorithm based on multi-agent deep reinforcement learning.
Chen Chen, Liu Haiying, Hou Hongkai, Qin Le
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An Efficient Centralized Multi-Agent Reinforcement Learner for Cooperative Tasks

IEEE Access, 2023
Multi-agent reinforcement learning (MARL) for cooperative tasks has been extensively researched over the past decade. The prevalent framework for MARL algorithms is centralized training and decentralized execution.
Dengyu Liao, Zhen Zhang, Tingting Song, Mingyang Liu   +3 more
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Reward-Reinforced Reinforcement Learning for Multi-agent Systems

CoRR, 2021
9 pages, 9 ...
Zheng, C., Yang, S., Parra-Ullauri, J., Garcia-Dominguez, A., Bencomo, N.   +4 more
openaire   +3 more sources

Lenient Multi-Agent Deep Reinforcement Learning [PDF]

International Joint Conference on Autonomous Agents and Multiagent Systems, 2018
Much of the success of single agent deep reinforcement learning (DRL) in recent years can be attributed to the use of experience replay memories (ERM), which allow Deep Q-Networks (DQNs) to be trained efficiently through sampling stored state transitions. However, care is required when using ERMs for multi-agent deep reinforcement learning (MA-DRL), as
G. Palmer (Gregory), K. Tuyls (Karl), D. Bloembergen (Daniel), R. Savani (Rahul)   +3 more
openaire   +4 more sources

Adaptive Reward Method for End-to-End Cooperation Based on Multi-agent Reinforcement Learning [PDF]

Jisuanji kexue, 2022
At present,most multi-agent reinforcement learning(MARL) algorithms using the architecture of centralized training and decentralized execution(CTDE) have good results in homogeneous multi-agent systems.However,for heterogeneous multi-agent systems ...
SHI Dian-xi, ZHAO Chen-ran, ZHANG Yao-wen, YANG Shao-wu, ZHANG Yong-jun
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Dynamic optimization of stand structure in Pinus yunnanensis secondary forests based on deep reinforcement learning and structural prediction

Frontiers in Plant Science
IntroductionThe rational structure of forest stands plays a crucial role in maintaining ecosystem functions, enhancing community stability, and ensuring sustainable management.
Jian Zhao, Jianming Wang, Jiting Yin, Yuling Chen, Baoguo Wu   +4 more
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Multi‐agent reinforcement learning based transmission scheme for IRS‐assisted multi‐UAV systems

IET Communications, 2023
In this paper, a transmission scheme based on multi‐agent reinforcement learning for intelligent reflecting surface (IRS)‐assisted multiple unmanned aerial vehicles (UAVs) systems is proposed.
Yumo Mei, Chen Liu, Yunchao Song, Ge Wang, Huibin Liang   +4 more
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