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VERSION:2.0
PRODID:-//University of Liverpool Computer Science Seminar System//v2//EN
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DTSTAMP:20260921T211255Z
UID:Seminar-DMML-1114@lxserverM.csc.liv.ac.uk
ORGANIZER:CN=Danushka Bollegala:MAILTO:Danushka.Bollegala@liverpool.ac.uk
DTSTART:20210908T110000
DTEND:20210908T120000
SUMMARY:Data Mining and Machine Learning Series
DESCRIPTION:Bei Peng: Cooperative Deep Multi-Agent Reinforcement Learning\n\nMany real-world learning problems involve multiple agents acting and interacting in the same environment to achieve some common goal, which can be naturally modeled as cooperative multi-agent systems. In this talk I will first overview some of the key challenges in cooperative multi-agent reinforcement learning. I will then describe the problem setting we focus on and the training paradigm we usually use to learn in such settings. One critical challenge in this setting is how to represent and learn the complex joint value functions. I will talk about two deep multi-agent reinforcement learning algorithms we developed recently to address this challenge. Finally, I will present Multi-Agent MuJoCo, a new comprehensive benchmark suite that we developed, based on the popular single-agent MuJoCo benchmark, to allow the study of decentralised continuous control. We believe it can potentially stimulate more progress in continuous multi-agent reinforcement learning.\n\nhttps://www.csc.liv.ac.uk/research/seminars/abstract.php?id=1114
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