Machine Learning, Functions and Goals

IF 0.2 4区 哲学 0 PHILOSOPHY Croatian Journal of Philosophy Pub Date : 2022-12-27 DOI:10.52685/cjp.22.66.5
Patrick Butlin
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引用次数: 2

Abstract

Machine learning researchers distinguish between reinforcement learning and supervised learning and refer to reinforcement learning systems as “agents”. This paper vindicates the claim that systems trained by reinforcement learning are agents while those trained by supervised learning are not. Systems of both kinds satisfy Dretske’s criteria for agency, because they both learn to produce outputs selectively in response to inputs. However, reinforcement learning is sensitive to the instrumental value of outputs, giving rise to systems which exploit the effects of outputs on subsequent inputs to achieve good performance over episodes of interaction with their environments. Supervised learning systems, in contrast, merely learn to produce better outputs in response to individual inputs.
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机器学习,功能和目标
机器学习研究人员区分强化学习和监督学习,并将强化学习系统称为“代理”。本文证明了通过强化学习训练的系统是代理,而通过监督学习训练的不是代理的说法。这两种系统都满足Dretske的代理标准,因为它们都学会了根据输入有选择地产生输出。然而,强化学习对输出的工具价值很敏感,导致系统利用输出对后续输入的影响,在与环境的互动中取得良好的表现。相比之下,有监督的学习系统只是学会根据个人输入产生更好的输出。
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CiteScore
0.20
自引率
0.00%
发文量
15
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