Results 21 to 30 of about 137,924 (252)
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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Lenient Multi-Agent Deep Reinforcement Learning [PDF]
, 2017 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
Palmer, Gregory +3 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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Mediated Multi-Agent Reinforcement Learning
, 2023 The majority of Multi-Agent Reinforcement Learning (MARL) literature equates the cooperation of self-interested agents in mixed environments to the problem of social welfare maximization, allowing agents to arbitrarily share rewards and private information.
Ivanov, Dmitry +2 more
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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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Multi-Agent Natural Actor-Critic Reinforcement Learning Algorithms
Dynamic Games and Applications, 2022 AbstractMulti-agent actor-critic algorithms are an important part of the Reinforcement Learning (RL) paradigm. We propose three fully decentralized multi-agent natural actor-critic (MAN) algorithms in this work. The objective is to collectively find a joint policy that maximizes the average long-term return of these agents.
Prashant Trivedi, Nandyala Hemachandra
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Frontiers in Plant ScienceIntroductionThe rational structure of forest stands plays a crucial role in maintaining ecosystem functions, enhancing community stability, and ensuring sustainable management.
Jian Zhao +4 more
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SC-MAIRL: Semi-Centralized Multi-Agent Imitation Reinforcement Learning
IEEE Access, 2023 Multi-agent reinforcement learning (MARL) is a challenging branch of reinforcement learning that requires cooperation of interactive learning agents to achieve individual objectives as well as shared team objectives.
Paul Brackett, Siming Liu, Yan Liu
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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 +4 more
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