{"type":"ScholarlyArticle","title":"Autonomously Reusing Knowledge in Multiagent Reinforcement Learning","authors":[{"name":"Felipe Leno Da Silva","slug":"felipe-leno-da-silva","given":"Felipe Leno","family":"Da Silva","affiliation":"University of São Paulo , São Paulo , Brazil"},{"name":"Matthew E. Taylor","slug":"matthew-e-taylor","given":"Matthew E.","family":"Taylor","affiliation":"Washington State University , Pullman , USA"},{"name":"Anna Helena Reali Costa","slug":"anna-helena-reali-costa","given":"Anna Helena Reali","family":"Costa","affiliation":"University of São Paulo , São Paulo , Brazil"}],"abstract":"Autonomous agents are increasingly required to solve complex tasks; hard-coding behaviors has become infeasible. Hence, agents must learn how to solve tasks via interactions with the environment. In many cases, knowledge reuse will be a core technology to keep training times reasonable, and for that, agents must be able to autonomously and consistently reuse knowledge from multiple sources, including both their own previous internal knowledge and from other agents. In this paper, we provide a literature review of methods for knowledge reuse in Multiagent Reinforcement Learning. We define an important challenge problem for the AI community, survey the existent methods, and discuss how they can all contribute to this challenging problem. Moreover, we highlight gaps in the current literature, motivating \"low-hanging fruit'' for those interested in the area. Our ambition is that this paper will encourage the community to work on this difficult and relevant research challenge.","doi":"10.24963/ijcai.2018/774","url":"https://ijcai.org/proceedings/2018/774","pdf":"https://ijcai.org/proceedings/2018/0774.pdf","markdown":"https://ijcai.org/proceedings/2018/774.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/774","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Survey track","track":"Survey track","number":774,"pages":{"first":5487,"last":5493},"keywords":["Machine Learning: Transfer, Adaptation, Multi-task Learning","Machine Learning: Reinforcement Learning","Agent-based and Multi-agent Systems: Multi-agent Learning"],"videos":[],"references":[{"position":1,"raw":"[Abbeel et al., 2007] Pieter Abbeel, Adam Coates, Morgan Quigley, and Andrew Y. 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