Results 11 to 20 of about 137,924 (252)

Environmental-Impact-Based Multi-Agent Reinforcement Learning

Applied Sciences
To promote cooperation and strengthen the individual impact on the collective outcome in social dilemmas, we propose the Environmental-impact Multi-Agent Reinforcement Learning (EMuReL) method where each agent estimates the “environmental impact” of ...
Farinaz Alamiyan-Harandi, Pouria Ramazi
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Intent-Aware Multi-Agent Reinforcement Learning [PDF]

2018 IEEE International Conference on Robotics and Automation (ICRA), 2018
ICRA ...
Qi, Siyuan, Zhu, Song-Chun
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Learning structured communication for multi-agent reinforcement learning

Autonomous Agents and Multi-Agent Systems, 2022
This work explores the large-scale multi-agent communication mechanism under a multi-agent reinforcement learning (MARL) setting. We summarize the general categories of topology for communication structures in MARL literature, which are often manually specified.
Junjie Sheng, Xiangfeng Wang, Bo Jin, Junchi Yan, Wenhao Li, Tsung-Hui Chang, Jun Wang, Hongyuan Zha   +7 more
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Stigmergy in Multi Agent Reinforcement Learning [PDF]

Fourth International Conference on Hybrid Intelligent Systems (HIS'04), 2005
In this paper, we describe how certain aspects of the biological phenomena of stigmergy can be imported into multi-agent reinforcement learning (MARL), with the purpose of better enabling coordination of agent actions and speeding up learning. In particular, we detail how these stigmergic aspects can be used to define an inter-agent communication ...
Aras, Raghav, Dutech, Alain, Charpillet, François   +2 more
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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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A proposal to use reinforcement learning to optimize decision-making in the field of counteracting money laundering and terrorist financing (Part 2)

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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Multi-Agent Deep Reinforcement Learning for Multi-Robot Applications: A Survey

Sensors, 2023
Deep reinforcement learning has produced many success stories in recent years. Some example fields in which these successes have taken place include mathematics, games, health care, and robotics. In this paper, we are especially interested in multi-agent
James Orr, Ayan Dutta
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Language Support for Multi Agent Reinforcement Learning [PDF]

Proceedings of the 13th Innovations in Software Engineering Conference (formerly known as India Software Engineering Conference), 2020
Software Engineering must increasingly address the issues of complexity and uncertainty that arise when systems are to be deployed into a dynamic software ecosystem. There is also interest in using digital twins of systems in order to design, adapt and control them when faced with such issues.
Clark, Tony, Barn, Balbir, Kulkarni, Vinay, Barat, Souvik   +3 more
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Review of Research on Agent Training Methods Toward Human-Agent Collaboration [PDF]

Jisuanji kexue
Human-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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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 ...
Ahmed, Ibrahim H., Brewitt, Cillian, Carlucho, Ignacio, Christianos, Filippos, Dunion, Mhairi, Fosong, Elliot, Garcin, Samuel, Guo, Shangmin, Gyevnar, Balint, McInroe, Trevor, Papoudakis, Georgios, Rahman, Arrasy, Schäfer, Lukas, Tamborski, Massimiliano, Vecchio, Giuseppe, Wang, Cheng, Albrecht, Stefano V.   +16 more
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