Suchergebnisse - Multi-robot Reinforcement Learning

Safe multi-agent reinforcement learning for multi-robot control von Gu, Shangding, Grudzien Kuba, Jakub, Chen, Yuanpei, Du, Yali, Yang, Long, Knoll, Alois, Yang, Yaodong

“… Yet, developing multi-robot control methods from the perspective of safe multi-agent reinforcement learning (MARL …”
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Multi-Agent Deep Reinforcement Learning for Multi-Robot Applications: A Survey von Orr, James, Dutta, Ayan

“… objective of finishing the task. Although multi-agent deep reinforcement learning and its applications in multi …”
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Distributed multi-agent deep reinforcement learning for cooperative multi-robot pursuit von Yu, Chao, Dong, Yinzhao, Li, Yangning, Chen, Yatong

“… This study the problem of multi-robot pursuit game using reinforcement learning (RL) techniques is studied …”
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MRCDRL: Multi-robot coordination with deep reinforcement learning von Wang, Di, Deng, Hongbin, Pan, Zhenhua

“… This paper proposes a multi-robot cooperative algorithm based on deep reinforcement learning (MRCDRL …”
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Lyapunov-Informed Multi-Agent Reinforcement Learning for Multi-Robot Cooperation Tasks von Feng, Pu, Shi, Rongye, Wang, Size, Wu, Qizhen, Yu, Xin, Wu, Wenjun

“… Multi-Agent Reinforcement Learning (MARL) has shown great potential in solving complex tasks …”
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Hierarchical Consensus-Based Multi-Agent Reinforcement Learning for Multi-Robot Cooperation Tasks von Feng, Pu, Liang, Junkang, Wang, Size, Yu, Xin, Ji, Xin, Chen, Yiting, Zhang, Kui, Shi, Rongye, Wu, Wenjun

“… In multi-agent reinforcement learning (MARL), the Centralized Training with Decentralized Execution (CTDE …”
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Multi‐station multi‐robot task assignment method based on deep reinforcement learning von Zhang, Junnan, Wang, Ke, Mu, Chaoxu

“… This paper focuses on the problem of multi‐station multi‐robot spot welding task assignment, and proposes a deep reinforcement learning (DRL …”
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A Mixed-Reality-Augmented Deep Reinforcement Learning Approach for Multi-Robot Safe Motion Generation in Human-Robot Collaborative Manufacturing Cells von Li, Chengxi, Yin, Yue, Ye, Hantao, Zheng, Pai, Gupta, Satyandra K.

“… To address these issues, this paper introduces the Deep Reinforcement Learning (DRL) approach for end-to-end safe motion generation in human multi-robot collaborative workspaces …”
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PD-FAC: Probability Density Factorized Multi-Agent Distributional Reinforcement Learning for Multi-Robot Reliable Search von Sheng, Wenda, Guo, Hongliang, Yau, Wei-Yun, Zhou, Yingjie

“… This letter presents a new range of multi-robot search for a non-adversarial moving target problems, namely multi-robot reliable search (MuRRS …”
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Reinforcement learning–based task allocation and path‐finding in multi‐robot systems under environment uncertainty von Huang, Songjun, Sun, Chuanneng, Gong, Jie, Pompili, Dario

“… Autonomous robots have the potential to significantly improve the operational efficiency of multi‐robot systems (MRSs …”
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Multi-robot path planning based on a deep reinforcement learning DQN algorithm von Yang, Yang, Juntao, Li, Lingling, Peng

“… to generate target Q-values to solve the problem of multi-robot path planning. The aim of the Q-learning algorithm in deep reinforcement learning is to address two shortcomings of the robot path-planning problem …”
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From Agents to Robots: A Training and Evaluation Platform for Multi-robot Reinforcement Learning von Liang, Zhiuxan, Cao, Jiannong, Jiang, Shan, Saxena, Divya, Cao, Rui, Xu, Huafeng

“… Multi-robot reinforcement learning (MRRL) is a promising approach to solving cooperation problems and has been widely adopted in many applications …”
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Behavioral control task supervisor with memory based on reinforcement learning for human—multi-robot coordination systems von Huang, Jie, Mo, Zhibin, Zhang, Zhenyi, Chen, Yutao

“… In this study, a novel reinforcement learning task supervisor (RLTS) with memory in a behavioral control framework is proposed for human …”
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Transformer-Based Reinforcement Learning for Multi-Robot Autonomous Exploration von Chen, Qihong, Wang, Rui, Lyu, Ming, Zhang, Jie

“… Our multi-agent deep reinforcement learning method includes a multi-agent learning method to effectively improve exploration efficiency …”
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Decision Making for Multi-Robot Fixture Planning Using Multi-Agent Reinforcement Learning von Canzini, Ethan, Auledas-Noguera, Marc, Pope, Simon, Tiwari, Ashutosh

“… In this paper, we present a framework for multi-agent reinforcement learning with team decision theory to find optimal fixturing plans for manufacturing tasks …”
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MAMBPO: Sample-efficient multi-robot reinforcement learning using learned world models von Willemsen, Daniel, Coppola, Mario, de Croon, Guido C.H.E.

“… Multi-robot systems can benefit from reinforcement learning (RL) algorithms that learn behaviours in a small number of trials, a property known as sample efficiency …”
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Heterogeneous Multi-Robot Cooperation With Asynchronous Multi-Agent Reinforcement Learning von Zhang, Han, Zhang, Xiaohui, Feng, Zhao, Xiao, Xiaohui

“… In this letter, we introduce a novel architecture for multi-robot decision-making and control based on multi-agent reinforcement learning (MARL …”
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Bayesian Reinforcement Learning for Multi-Robot Decentralized Patrolling in Uncertain Environments von Zhou, Xin, Wang, Weiping, Wang, Tao, Lei, Yonglin, Zhong, Fangcheng

“… The use of such team of robots to perform well is a challenging problem. To address the challenge, a decentralized multi-robot patrolling problem in uncertain environments without prior knowledge is investigated in this paper …”
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Centralizing State-Values in Dueling Networks for Multi-Robot Reinforcement Learning Mapless Navigation von Marchesini, Enrico, Farinelli, Alessandro

“… We study the problem of multi-robot mapless navigation in the popular Centralized Training and Decentralized Execution (CTDE) paradigm …”
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Cooperative Multi-Robot Hierarchical Reinforcement Learning von Setyawan, Gembong Edhi, Hartono, Pitoyo, Sawada, Hideyuki

“… Recent advances in multi-robot deep reinforcement learning have made it possible to perform efficient exploration in problem space, but it remains a significant challenge in many complex domains …”
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