Deep reinforcement learning for multi-agent interaction

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

doi:10.3233/aic-220116

Cited by 6 publications

(3 citation statements)

References 31 publications

(43 reference statements)

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“…It has been a powerful tool for studying complex task decision-making. Ahmed studied the effective interaction system between agents [30]. The information obtained in the real world was not necessarily accurate.…”

Section: A Related Workmentioning

confidence: 99%

A Sequential Decision Algorithm of Reinforcement Learning for Composite Action Space

Gao,

Wang,

Zhang

et al. 2023

IEEE Access

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It is the key research object of electronic warfare to use UAV( Unmanned Aerial Vehicle) clusters to carry out electronic countermeasure tasks. The UAV carries loads such as reconnaissance and interference at the same time, which makes it necessary to simultaneously decide multiple types of actions-namely, compound actions-which poses a challenge to intelligent decision-making algorithms.Considering the problem of action-space dimensional complexity and weak collaboration between decisions in multi-agent scenarios with composite actions, this study proposed a decision algorithm involving a multiagent reinforcement-learning sequence, which combined joint composite actions into sequential decision, reducing the difficulty of a single decision and enhancing the collaboration between various agents and their individual decisions. Because long decision sequences required better depth modeling and had high variance, a DeLighT module was added to the naïve transformer model to increase the depth and baseline techniques, which were used to reduce the variance in the value estimation. The simulated results verified the effectiveness of the proposed algorithm in the UAV cooperative combat scenario, where each agent had a composite action space and showed better performance than the existing algorithms.

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Section: A Related Workmentioning

confidence: 99%

A Sequential Decision Algorithm of Reinforcement Learning for Composite Action Space

Gao,

Wang,

Zhang

et al. 2023

IEEE Access

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“…Reinforcement learning techniques are extensively employed in motion planning systems for both individual robots and their collectives [36][37][38][39][40][41]. However, existing fi ndings Citation: Maevskiy As a result, the authors of the paper opted to depart from the conventional approach of constructing a "hard-coded system" for forming MRCs and instead adopted a method based on AGI (the Area of Group Interaction).…”

Section: Introductionmentioning

confidence: 99%

Intelligent complex for modeling safety movement of a multi-agent group of marine robots in uncertain environments

Maevskiy,

Kozhemyakin,

Zanin

2024

J Civil Eng Environ Sci

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Intensive development today is observed in the areas of data processing and analysis, as well as in the management of robotic systems; Artificial Intelligence (AI) systems are increasingly used. As the complexity of the tasks facing the Marine Robotic Complex (MRC) increases, the need for the use of AI technologies becomes clear. These technologies provide safe movement and control of marine objects, navigation of MRC in maritime space, development of behavior logic in unknown environments and planning of their movement, as well as optimization of data processing. The development of MRC currently covers various areas for which there is no single solution yet. This article outlines the development process of a simulation complex designed to model an intelligent system (IS) for strategizing the motion of a collective of marine robotic systems. It includes an in-depth exposition of individual modules and the mathematical modeling of the employed algorithms.

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“…• DeepMind [7] • Five AI [9] • Heriot-Watt University [13] • King's College London [2] • Teesside University [8] • University of Aberdeen [3] • University of Edinburgh [1] • University of Essex [11] • University of Lancaster [4]…”

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confidence: 99%

Multi-agent systems research in the United Kingdom

Albrecht

Wooldridge

2022

AIC

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Multi-agent systems have been a core research topic in artificial intelligence for several decades. A multiagent system consists of multiple decision-making agents -which may be software-based AI systems, physicallyembodied robots, or humans -which must interact in a shared environment in pursuit of their goals. Multi-agent systems research spans a range of technical problems, such as how to design planning and learning algorithms which enable agents to achieve their goals; how to design multi-agent systems to incentivise certain behaviours in agents; how information is communicated and propagated among agents; and how norms, conventions, and roles may emerge in multi-agent systems. A vast array of applications have been addressed using multi-agent methodologies, including autonomous driving, multi-robot factories, automated trading, commercial games, automated tutoring, and robotic rescue teams.The purpose of this special issue is to showcase current multi-agent systems research led by university and industry groups based in the United Kingdom. Research groups and institutes in the UK which have significant activity in multi-agent systems research were invited to submit an article describing: (1) the technical problems in multi-agent systems tackled by the group (their core research agenda), including applications and industry collaboration; (2) the main approaches developed by the group and any key results achieved; and (3) important open challenges in multi-agent systems research from the perspective of the group.A large number of high-quality submissions were received, of which 14 were included for publication in the special issue. These articles represent a broad set of research topics within the field of multi-agent systems, showcasing the strength of contributions made by UK-based research groups in both universities and industry. We believe the open research problems discussed in each of the articles will provide a rich resource for researchers in this field, both new and old.Research groups from the following organisations are represented in the special issue (ordered alphabetically

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Deep reinforcement learning for multi-agent interaction

Cited by 6 publications

References 31 publications

A Sequential Decision Algorithm of Reinforcement Learning for Composite Action Space

A Sequential Decision Algorithm of Reinforcement Learning for Composite Action Space

Intelligent complex for modeling safety movement of a multi-agent group of marine robots in uncertain environments

Multi-agent systems research in the United Kingdom

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