Uncertainty-driven state space exploration for reinforcement learning = 강화 학습을 위한 불확실성 기반 상태 공간 탐색 알고리즘

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Metacognition is seen as the human’s capability to introspect their thought process and report their level of uncertainty/confidence in the course of learning. The metacognitive ability can be extremely useful in guiding behaviour during learning, in deciding whether to explore a new alternative or stick with the current one. In the past few years, the neuroscientific community has made some progress in understanding the neural basis of uncertainty/confidence representation. However, little is known about how uncertainty/confidence arises at the computational level during reinforcement learning. Here we propose to combine machine learning with behavioural data to characterise the exact computational steps that underlie the psychological construction of uncertainty during learning in complex environments, also aim to design a formal model for human’s state space learning process based on metacognition. The central aim of this work is to provide a mechanistic understanding of how uncertainty is constructed at the algorithmic level by the human brain and how it is used to drive learning.
Advisors
Lee, Sang Wanresearcher이상완researcherJeong, Jae Seungresearcher정재승researcher
Description
한국과학기술원 :뇌인지공학프로그램,
Publisher
한국과학기술원
Issue Date
2017
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 뇌인지공학프로그램, 2017.8,[iii, 43 p. :]

Keywords

Learning▼aUncertainty▼aMetacognition▼aExploration-Exploitation dilemma▼aDecision making; 학습▼a불확실성▼a메타 인지▼aExploration-Exploitation dilemma▼a의사 결정

URI
http://hdl.handle.net/10203/243002
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=718757&flag=dissertation
Appears in Collection
BiS-Theses_Master(석사논문)
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