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arXiv:2205.06175cs.AI

A Generalist Agent

Scott Reed, Konrad Zolna, Emilio Parisotto, Sergio Gomez Colmenarejo, Alexander Novikov, Gabriel Barth-Maron, Mai Gimenez, Yury Sulsky, Jackie Kay, Jost Tobias Springenberg, Tom Eccles, Jake Bruce, Ali Razavi, Ashley Edwards, Nicolas Heess, Yutian Chen, Raia Hadsell, Oriol Vinyals, Mahyar Bordbar, Nando de Freitas

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Abstract

Inspired by progress in large-scale language modeling, we apply a similar approach towards building a single generalist agent beyond the realm of text outputs. The agent, which we refer to as Gato, works as a multi-modal, multi-task, multi-embodiment generalist policy. The same network with the same weights can play Atari, caption images, chat, stack blocks with a real robot arm and much more, deciding based on its context whether to output text, joint torques, button presses, or other tokens. In this report we describe the model and the data, and document the current capabilities of Gato.

arXiv comments
Published at TMLR, 42 pages
Journal reference
Transactions on Machine Learning Research, 11/2022, https://openreview.net/forum?id=1ikK0kHjvj
DOI
Subjects
cs.AIArtificial Intelligence cs.CLComputation and Language cs.LGMachine Learning cs.RORobotics

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Linked by 1 identifiable commenter in 1 comment across 1 thread, from Aug 2022 to Aug 2022. Found in 1 HN submission from May 2022 to May 2022. Open submission discussions.

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    • See: https://arxiv.org/abs/2205.06175 A Generalist Agent Inspired by progress in large-scale language modeling, we apply a similar approach towards building a single generalist agent beyond the realm of text outputs. The agent, which we refer to as Gato, works as a multi-modal…

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