Results 11 to 20 of about 10,350,910 (281)
Distributed learning for multi-agent control of a dynamic system [PDF]
, 2011 This thesis describes an investigation of self-organising, distributed control of dynamic, non-linear systems. The distribution is achieved through a multi-agent based approach. The self-organisation is addressed through reinforcement learning.
Pay, Mungo L
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Deep multi-agent reinforcement learning [PDF]
, 2018 A plethora of real world problems, such as the control of autonomous vehicles and drones, packet delivery, and many others consists of a number of agents that need to take actions based on local observations and can thus be formulated in the multi-agent reinforcement learning (MARL) setting. Furthermore, as more machine learning systems are deployed in
Jakob N Foerster, Foerster, Jakob N
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Multi-Agent Reinforcement Learning: A Survey [PDF]
2006 9th International Conference on Control, Automation, Robotics and Vision, 2006 Multi-agent systems are rapidly finding applications in a variety of domains, including robotics, distributed control, telecommunications, economics. Many tasks arising in these domains require that the agents learn behaviors online. A significant part of the research on multi-agent learning concerns reinforcement learning techniques.
Lucian Busoniu +2 more
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Reinforcement Learning-based Spectrum Sharing for Cognitive Radio [PDF]
, 2011 This thesis investigates how distributed reinforcement learning-based resource assignment algorithms can be used to improve the performance of a cognitive radio system.
Jiang, Tao
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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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Mediated Multi-Agent Reinforcement Learning
International Joint Conference on Autonomous Agents and Multiagent Systems, 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.
Dmitry Ivanov +2 more
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Coordination and communication in deep multi-agent reinforcement learning [PDF]
, 2022 A growing number of real-world control problems require teams of software agents to solve a joint task through cooperation. Such tasks naturally arise whenever human workers are replaced by machines, such as robot arms in manufacturing or autonomous cars
Schroeder de Witt, Christian A
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Learning structured communication for multi-agent reinforcement learning
Autonomous Agents and Multi-Agent Systems, 2022 This paper investigates multi-agent reinforcement learning (MARL) communication mechanisms in large-scale scenarios. We propose a novel framework, Learning Structured Communication (LSC), that leverages a flexible and efficient communication topology. LSC enables adaptive agent grouping to create diverse hierarchical formations over episodes generated ...
Junjie Sheng +7 more
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Nowoczesne Systemy Zarządzania, 2023 Reinforcement learning focuses not only on teaching a single agent, but also the use of this method is reflected in multi-agent operation. This is an important issue from the point of view that the decision-making process and information management in ...
Maciej Aleksander Kędzierski
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Heterogeneous multi-Agent reinforcement learning algorithm integrating Prior-knowledge [PDF]
Zhihui kongzhi yu fangzhen, 2023 In recent years, the breakthrough of machine learning based on deep reinforcement learning provides a new development direction for intelligent game confrontation.
ZHOU Jiawei, SUN Yuxiang, XUE Yufan, XIANG Qi, WU Ying, ZHOU Xianzhong
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