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Theory-Based Reinforcement Learning: A Computational Framework for Modeling Human Inductive Biases in Complex Decision Making Domains.
Theory-Based Reinforcement Learning: A Computational Framework for Modeling Human Inductive Biases in Complex Decision Making Domains.
- 자료유형
- 학위논문
- Control Number
- 0016616862
- International Standard Book Number
- 9798819381250
- Dewey Decimal Classification Number
- 153
- Main Entry-Personal Name
- Pouncy, Thomas.
- Publication, Distribution, etc. (Imprint
- [S.l.] : Harvard University., 2022
- Publication, Distribution, etc. (Imprint
- Ann Arbor : ProQuest Dissertations & Theses, 2022
- Physical Description
- 165 p.
- General Note
- Source: Dissertations Abstracts International, Volume: 83-12, Section: B.
- General Note
- Advisor: Gershman, Samuel.
- Dissertation Note
- Thesis (Ph.D.)--Harvard University, 2022.
- Restrictions on Access Note
- This item must not be sold to any third party vendors.
- Subject Added Entry-Topical Term
- Cognitive psychology.
- Subject Added Entry-Topical Term
- Artificial intelligence.
- Subject Added Entry-Topical Term
- Psychology.
- Subject Added Entry-Topical Term
- Computer science.
- Index Term-Uncontrolled
- Inductive biases
- Index Term-Uncontrolled
- Reinforcement learning
- Index Term-Uncontrolled
- Sequential decision making
- Index Term-Uncontrolled
- Structure learning
- Index Term-Uncontrolled
- Complex tasks
- Added Entry-Corporate Name
- Harvard University Psychology
- Host Item Entry
- Dissertations Abstracts International. 83-12B.
- Host Item Entry
- Dissertation Abstract International
- Electronic Location and Access
- 로그인을 한후 보실 수 있는 자료입니다.
- Control Number
- joongbu:622923
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