Please use this identifier to cite or link to this item:
|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)|
Updated on May 17, 2022
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.