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• W2921955147 startingPage "645" @default.
• W2921955147 abstract "&#x0D; &#x0D; &#x0D; Multiagent Reinforcement Learning (RL) solves complex tasks that require coordination with other agents through autonomous exploration of the environment. However, learning a complex task from scratch is impractical due to the huge sample complexity of RL algorithms. For this reason, reusing knowledge that can come from previous experience or other agents is indispensable to scale up multiagent RL algorithms. This survey provides a unifying view of the literature on knowledge reuse in multiagent RL. We define a taxonomy of solutions for the general knowledge reuse problem, providing a comprehensive discussion of recent progress on knowledge reuse in Multiagent Systems (MAS) and of techniques for knowledge reuse across agents (that may be actuating in a shared environment or not). We aim at encouraging the community to work towards reusing all the knowledge sources available in a MAS. For that, we provide an in-depth discussion of current lines of research and open questions.&#x0D; &#x0D; &#x0D;" @default.
• W2921955147 created "2019-03-22" @default.
• W2921955147 creator A5053630715 @default.
• W2921955147 creator A5069264027 @default.
• W2921955147 date "2019-03-11" @default.
• W2921955147 modified "2023-10-13" @default.
• W2921955147 title "A Survey on Transfer Learning for Multiagent Reinforcement Learning Systems" @default.
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