Results 21 to 30 of about 139,994 (273)
Deep reinforcement learning for multi-agent interaction
AI Communications, 2022 The development of autonomous agents which can interact with other agents to accomplish a given task is a core area of research in artificial intelligence and machine learning. Towards this goal, the Autonomous Agents Research Group develops novel machine learning algorithms for autonomous systems control, with a specific focus on deep reinforcement ...
Ibrahim H. Ahmed 0006 +16 more
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On Centralized Critics in Multi-Agent Reinforcement Learning
Journal of Artificial Intelligence Research, 2023 Centralized Training for Decentralized Execution, where agents are trained offline in a centralized fashion and execute online in a decentralized manner, has become a popular approach in Multi-Agent Reinforcement Learning (MARL). In particular, it has become popular to develop actor-critic methods that train decentralized actors with a centralized ...
Xueguang Lyu +4 more
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Multi-agent Learning and the Reinforcement Gradient [PDF]
, 2012 This article shows that seemingly diverse implementations of multi-agent reinforcement learning share the same basic building block in their learning dynamics: a mathematical term that is closely related to the gradient of the expected reward. Gradient Ascent on the expected reward has been used to derive strong convergence results in two-player two ...
Michael Kaisers +2 more
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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 kexueHuman-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
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Hierarchical multi‐agent reinforcement learning for multi‐aircraft close‐range air combat
IET Control Theory & Applications, 2023 The close‐range autonomous air combat has gained significant attention from researchers involved in applications related to artificial intelligence (AI).
Wei‐ren Kong +4 more
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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 +4 more
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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 +3 more
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Hybrid autonomous control for heterogeneous multi-agent system [PDF]
, 2003 Reinforcement learning is an adaptive and flexible control method for autonomous system. In our previous works, we had proposed a reinforcement learning algorithm for redundant systems: "Q-learning with dynamic structuring of exploration space based
Gofuku, Akio, Ito, Kazuyuki
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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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