Deep Reinforcement Learning for Decision Neuroscience – Faizanshaikh Abdulkhalil SHAIKH
Faizanshaikh Abdulkhalil SHAIKH
Master's thesis
Deep Reinforcement Learning for Decision Neuroscience
Abstract:
The study investigates human decision-making behaviour within a game-based context and endeavours to replicate said behaviour using the Generative Adversarial Imitation Learning (GAIL) technique. In this gamified environment, inspired by a hunter-gatherer scenario, players have to ensure their survival in a challenging environment while accounting for their episodic homeostasis and factoring in current …moreAbstract:
The study investigates human decision-making behaviour within a game-based context and endeavours to replicate said behaviour using the Generative Adversarial Imitation Learning (GAIL) technique. In this gamified environment, inspired by a hunter-gatherer scenario, players have to ensure their survival in a challenging environment while accounting for their episodic homeostasis and factoring in current …more
Language used: English
Date on which the thesis was submitted / produced: 8. 2. 2024
Thesis defence
- Supervisor: prof. Dr. Patrick Glauner
Citation record
ISO 690-compliant citation record:
SHAIKH, Faizanshaikh Abdulkhalil. \textit{Deep Reinforcement Learning for Decision Neuroscience}. Online. Master's thesis. České Budějovice: University of South Bohemia in České Budějovice, Faculty of Science. 2024. Available from: https://theses.cz/id/gpi6fp/.
The right form of listing the thesis as a source quoted
SHAIKH, Faizanshaikh Abdulkhalil. Deep Reinforcement Learning for Decision Neuroscience. České Budějovice, 2024. diplomová práce (Mgr.). JIHOČESKÁ UNIVERZITA V ČESKÝCH BUDĚJOVICÍCH. Přírodovědecká fakulta
Full text of thesis
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Institution archiving the thesis and making it accessible: JIHOČESKÁ UNIVERZITA V ČESKÝCH BUDĚJOVICÍCH, Přírodovědecká fakultaUNIVERSITY OF SOUTH BOHEMIA IN ČESKÉ BUDĚJOVICE
Faculty of ScienceMaster programme / field:
Artificial Intelligence and Data Science / Artificial Intelligence and Data Science
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