Results 31 to 40 of about 139,994 (273)
Cooperative Multi-Agent Systems from the Reinforcement Learning Perspective -- Challenges, Algorithms, and an Application [PDF]
, 2010 Reinforcement Learning has established as a framework that allows an autonomous agent for automatically acquiring -- in a trial and error-based manner -- a behavior policy based on a specification of the desired behavior of the system.
Gabel, Thomas
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Reward-Reinforced Reinforcement Learning for Multi-agent Systems
CoRR, 2021 9 pages, 9 ...
Changgang Zheng +4 more
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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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Multi-Agent Deep Reinforcement Learning with Human Strategies
, 2019 Deep learning has enabled traditional reinforcement learning methods to deal with high-dimensional problems. However, one of the disadvantages of deep reinforcement learning methods is the limited exploration capacity of learning agents.
Nahavandi, Saeid +2 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 +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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IET Smart Grid, 2022 Residential buildings are large consumers of energy. They contribute significantly to the demand placed on the grid, particularly during hours of peak demand.
Junlin Lu, Patrick Mannion, Karl Mason
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Measuring collaborative emergent behavior in multi-agent reinforcement learning
, 2018 Multi-agent reinforcement learning (RL) has important implications for the future of human-agent teaming. We show that improved performance with multi-agent RL is not a guarantee of the collaborative behavior thought to be important for solving multi ...
E Rovira +4 more
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Low-Cost Multi-Agent Navigation via Reinforcement Learning With Multi-Fidelity Simulator
IEEE Access, 2021 In recent years, reinforcement learning (RL) has been widely used to solve multi-agent navigation tasks, and a high-fidelity level for the simulator is critical to narrow the gap between simulation and real-world tasks.
Jiantao Qiu +6 more
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Consensus Learning for Cooperative Multi-Agent Reinforcement Learning
Proceedings of the AAAI Conference on Artificial Intelligence, 2023 Almost all multi-agent reinforcement learning algorithms without communication follow the principle of centralized training with decentralized execution. During the centralized training, agents can be guided by the same signals, such as the global state. However, agents lack the shared signal and choose actions given local observations during execution.
Zhiwei Xu 0005 +6 more
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