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The rapid rise of computational power allows ever more capable AI agents to be trained in simulation. A simulator, of course, does not fully reflect reality nor human preferences. How can AI agents learn useful, human-aligned behaviors in simulation that transfer to new settings and people?
I consider this question primarily in the setting of deep reinforcement learning, focusing on problems arising at the intersections of generalization, human-AI coordination, and open-ended systems.
I have the great fortune to be advised by Tim Rocktäschel and Edward Grefenstette and to work with many other fantastic collaborators
I consider this question primarily in the setting of deep reinforcement learning, focusing on problems arising at the intersections of generalization, human-AI coordination, and open-ended systems.
I have the great fortune to be advised by Tim Rocktäschel and Edward Grefenstette and to work with many other fantastic collaborators
研究兴趣
论文共 29 篇作者统计合作学者相似作者
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ICLR 2024 (2024)
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Mikayel Samvelyan, Sharath Chandra Raparthy,Andrei Lupu,Eric Hambro, Aram H. Markosyan, Manish Bhatt, Yuning Mao,Minqi Jiang, Jack Parker-Holder,Jakob Foerster,Tim Rocktäschel,Roberta Raileanu
CoRR (2024)
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CoRR (2024)
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Michael Beukman, Samuel Coward, Michael Matthews, Mattie Fellows,Minqi Jiang,Michael Dennis,Jakob Foerster
CoRR (2024)
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CoRR (2023)
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CoRR (2023): 12972-12999
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arxiv(2023)
Alexander Rutherford,Benjamin Ellis, Matteo Gallici,Jonathan Cook,Andrei Lupu, Garðar Ingvarsson,Timon Willi,Akbir Khan,Christian Schröder de Witt, Alexandra Souly,Saptarashmi Bandyopadhyay,Mikayel Samvelyan,
CoRR (2023)
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