Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/157373
Title: Deep learning for humanlike character motion control in VR table tennis
Authors: Tan, Wen Jie
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Engineering::Computer science and engineering::Computing methodologies::Simulation and modeling
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Tan, W. J. (2022). Deep learning for humanlike character motion control in VR table tennis. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157373
Project: SCSE21-0105
Abstract: In character motion control, reinforcement learning (RL) has provided new methods to create controllers for simulated characters. The latest research gives us a framework that creates controllers that are both humanlike and dynamic, which solves the initial problem of these RL based controllers. This framework however is only implemented using the Bullet physics engine, and thus cannot be directly implemented in game engines that use different physics engines, such as the Unity game engine. This project aims to improve on an adaptation of this framework on Unity by implementing it on a VR tennis game. The agent tries to mimic a given action and react to the environment around it. The different RL policies available are compared, and limitations of this adaptation on Unity are discussed as well.
URI: https://hdl.handle.net/10356/157373
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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