Towards Ad-Hoc Teamwork for Improved Player Experience | Sam Devlin

Towards Ad-Hoc Teamwork for Improved Player Experience (opens in new tab)
ICARL Seminar Series – 2022 Winter
Seminar by Sam Devlin

Abstract:
Collaborative multi-agent reinforcement learning research often makes two key assumptions: (1) we have control of all agents on the team; and (2) maximising team reward is all you need. However, to enable human-AI collaboration, we need to break both of these assumptions. In this talk I will formalise the problem of ad-hoc teamwork and present our proposed approach to meta-learn policies robust to a given set of possible future collaborators. Then talk about recent work on modelling human play, showing reward maximisation may not be sufficient when trying to entertain billions of players worldwide.

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Links
Sam Devlin
Site: aka.ms/samdevlin
Twitter: x.com/smdvln

ICARL
Site: icarl.doc.ic.ac.uk
Twitter: x.com/ic_arl
YouTube: youtube.com/ICARLSeminars
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Intro and Outro music courtesy of Bensound.com – Funky Suspense by Benjamin Tissot

日期：
演讲者：
Sam Devlin