Reinforcement learning and artificial agency

被引:4
|
作者
Butlin, Patrick [1 ,2 ]
机构
[1] Univ Oxford, Future Humanity Inst, Oxford, Oxfordshire, England
[2] Univ Oxford, Future Humanity Inst, Trajan House,Mill St, Oxford OX2 0DJ, Oxfordshire, England
关键词
action for reasons; agency; artificial intelligence; minimal agency; reinforcement learning; ORGANIZATIONAL ACCOUNT; GO; DISPOSITIONS; ANIMALS; REASON; SHOGI; CHESS;
D O I
10.1111/mila.12458
中图分类号
H0 [语言学];
学科分类号
030303 ; 0501 ; 050102 ;
摘要
There is an apparent connection between reinforcement learning and agency. Artificial entities controlled by reinforcement learning algorithms are standardly referred to as agents, and the mainstream view in the psychology and neuroscience of agency is that humans and other animals are reinforcement learners. This article examines this connection, focusing on artificial reinforcement learning systems and assuming that there are various forms of agency. Artificial reinforcement learning systems satisfy plausible conditions for minimal agency, and those which use models of the environment to perform forward search are capable of a form of agency which may reasonably be called action for reasons.
引用
收藏
页码:22 / 38
页数:17
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