Results 1 to 10 of about 137,924 (252)
Optimal User Scheduling in Multi Antenna System Using Multi Agent Reinforcement Learning [PDF]
Sensors, 2022 Multiple Input Multiple Output (MIMO) systems have been gaining significant attention from the research community due to their potential to improve data rates.
Muddasar Naeem +4 more
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Overview of Multi-agent Deep Reinforcement Learning Based on Value Factorization [PDF]
Jisuanji kexue, 2022 Multi-agent deep reinforcement learning based on value factorization is one of many multi-agent deep reinforcement learning algorithms,and it is also a research hotspot in the field of multi-agent deep reinforcement learning.Under some constraints,the ...
XIONG Li-qin, CAO Lei, LAI Jun, CHEN Xi-liang
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Multi-Goal Multi-Agent Deep Reinforcement Learning Method Based on Value Decomposition [PDF]
Jisuanji gongcheng, 2023 Multi-agent deep reinforcement learning method can be used in scenarios that require multi-party cooperation in the real world, which remains a challenge in the field of reinforcement learning.In these scenarios, agents usually have complex relationships
SONG Jian, WANG Zilei
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UAV Anti-tank Policy Training Model Based on Curriculum Reinforcement Learning [PDF]
Jisuanji kexue, 2023 In the intelligent era,the battle for land battlefield expands from planar land control to vertical land control.UAV anti-tank operation plays a crucial role in the battle for land control in future intelligent war.Deep reinforcement learning method in ...
LIN Zeyang, LAI Jun, CHEN Xiliang, WANG Jun
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Intelligent and Converged Networks, 2023 The adoption of the Fifth Generation (5G) and beyond 5G networks is driving the demand for learning approaches that enable users to co-exist harmoniously in a multi-user distributed environment. Although resource-constrained, the Cognitive Radio (CR) has
Kagiso Rapetswa, Ling Cheng
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Multi-agent reinforcement learning for traffic congestion on one-way multi-lane highways
Journal of Information and Telecommunication, 2023 In the last decade, agent-based modelling and simulation has emerged as a potential approach to study complex systems in the real world, such as traffic congestion.
Nguyen-Tuan-Thanh Le
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Multi-Agent Reinforcement Learning: A Review of Challenges and Applications
Applied Sciences, 2021 In this review, we present an analysis of the most used multi-agent reinforcement learning algorithms. Starting with the single-agent reinforcement learning algorithms, we focus on the most critical issues that must be taken into account in their ...
Lorenzo Canese +6 more
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Avoiding collaborative paradox in multi‐agent reinforcement learning
ETRI Journal, 2021 The collaboration productively interacting between multi‐agents has become an emerging issue in real‐world applications. In reinforcement learning, multi‐agent environments present challenges beyond tractable issues in single‐agent settings.
Hyunseok Kim +3 more
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Load Frequency Control: A Deep Multi-Agent Reinforcement Learning Approach [PDF]
, 2020 The paradigm shift in energy generation towards microgrid-based architectures is changing the landscape of the energy control structure heavily in distribution systems.
Alonso, E. +2 more
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An Overview of Cooperative Multi-Agent Deep Reinforcement Learning [PDF]
Hangkong bingqi, 2022 Multi-agent system is a distributed decision-making system composed of multi-agents interacting with environment, which is an important research direction of distributed artificial intelligence.
Zou Qijie, Jiang Yajun, Gao Bing, Li Wenxue, Zhang Rubo
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