Results 21 to 30 of about 10,350,910 (281)
Learning to Share in Multi-Agent Reinforcement Learning
CoRR, 2021 ICLR 2022 Workshop on Gamification and Multiagent Solutions, Best Cooperative AI Paper ...
Yuxuan Yi +3 more
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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 ...
Ibrahim H. Ahmed 0006 +16 more
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Fuzzy and tile coding approximation techniques for coevolution in reinforcement learning [PDF]
, 2005 PhDThis thesis investigates reinforcement learning algorithms suitable for learning in large state space problems and coevolution. In order to learn in large state spaces, the state space must be collapsed to a computationally feasible size and then ...
Tokarchuk, Laurissa Nadia
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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.
Tony Clark 0001 +3 more
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Optimal User Scheduling in Multi Antenna System Using Multi Agent Reinforcement Learning
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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Learning in multi-agent systems [PDF]
, 2001 In recent years, multi-agent systems (MASs) have received increasing attention in the artificial intelligence community. Research in multi-agent systems involves the investigation of autonomous, rational and flexible behaviour of entities such as ...
Alonso, E. +14 more
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Improving Exploration in Reinforcement Learning through Domain Knowledge and Parameter Analysis [PDF]
, 2010 This thesis presents novel work on how to improve exploration in reinforcement learning using domain knowledge and knowledge-based approaches to reinforcement learning.
Grzes, Marek
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Multi-agent Learning and the Reinforcement Gradient [PDF]
, 2012 This article shows that seemingly diverse implementations of multi-agent reinforcement learning share the same basic building block in their learning dynamics: a mathematical term that is closely related to the gradient of the expected reward. Gradient Ascent on the expected reward has been used to derive strong convergence results in two-player two ...
Michael Kaisers +2 more
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On Centralized Critics in Multi-Agent Reinforcement Learning
Journal of Artificial Intelligence Research, 2023 Centralized Training for Decentralized Execution, where agents are trained offline in a centralized fashion and execute online in a decentralized manner, has become a popular approach in Multi-Agent Reinforcement Learning (MARL). In particular, it has become popular to develop actor-critic methods that train decentralized actors with a centralized ...
Xueguang Lyu +4 more
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Hierarchical multi‐agent reinforcement learning for multi‐aircraft close‐range air combat
IET Control Theory & Applications, 2023 The close‐range autonomous air combat has gained significant attention from researchers involved in applications related to artificial intelligence (AI).
Wei‐ren Kong +4 more
doaj +1 more source