Original scientific paper
https://doi.org/10.52685/cjp.22.66.5
Machine Learning, Functions and Goals
Patrick Butlin
orcid.org/0000-0001-5837-5057
; University of Oxford, Oxford, UK
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.
Keywords
Agency; machine learning; reinforcement learning; artificial intelligence; Dretske.
Hrčak ID:
288525
URI
Publication date:
27.12.2022.
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