December 7, 2018
We propose to combine reinforcement learning and theoretical physics to describe effective theories of agency. This involves understanding the connection between the physics notion of causality and how intelligent agents can arise as a useful effective description within some environments. We discuss cases where such an effective theory of agency can break down and suggest a broader framework incorporating theory of mind for expanding the notion of agency in the presence of other agents that can predict actions. We comment on implications for superintelligence and whether physical bounds can be used to place limits on such predictors.
October 19, 2026
Muse Glimmer team
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Arturo Loaiza-Bonilla
October 14, 2026
October 14, 2026
Daniel Kim
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October 14, 2026
Paddy Ssentongo
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October 31, 2019
Peng-Jen Chen, Jiajun Shen, Matt Le, Vishrav Chaudhary, Ahmed El-Kishky, Guillaume Wenzek, Myle Ott, Marc’Aurelio Ranzato
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October 27, 2019
Zhuoyuan Chen, Demi Guo, Tong Xiao, Saining Xie, Xinlei Chen, Haonan Yu, Jonathan Gray, Kavya Srinet, Haoqi Fan, Jerry Ma, Charles R. Qi, Shubham Tulsiani, Arthur Szlam, Larry Zitnick
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April 25, 2020
Yilun Du, Joshua Meier, Jerry Ma, Rob Fergus, Alexander Rives
April 25, 2020
June 11, 2019
Yuandong Tian, Jerry Ma, Qucheng Gong, Shubho Sengupta, Zhuoyuan Chen, James Pinkerton, Larry Zitnick
June 11, 2019
