Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/156771
Title: Deep learning with intelligent opponent in fencing
Authors: Loh, Qiao Yan
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Loh, Q. Y. (2022). Deep learning with intelligent opponent in fencing. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/156771
Project: SCSE21-0099
Abstract: VR technologies have enabled the development of virtual sports games, which greatly enhance the accessibility of physical sport by making it available virtually. Considering there is currently no such game for fencing, this project aims to design and develop a VR fencing game. For now, we have designed and implemented a fencer avatar to act in the virtual environment and conducted several reinforcement learning experiments to train an intelligent fencing agent as the game opponent. Training of the agent is conducted in a self-play setting. The training result showed that there is a need to further improve the action space of the agent.
URI: https://hdl.handle.net/10356/156771
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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