Utilizing Motion Matching with Deep Reinforcement Learning for Target Location Tasks
| dc.contributor.author | Lee, Jeongmin | en_US |
| dc.contributor.author | Kwon, Taesoo | en_US |
| dc.contributor.author | Shin, Hyunju | en_US |
| dc.contributor.author | Lee, Yoonsang | en_US |
| dc.contributor.editor | Hu, Ruizhen | en_US |
| dc.contributor.editor | Charalambous, Panayiotis | en_US |
| dc.date.accessioned | 2024-04-30T08:18:55Z | |
| dc.date.available | 2024-04-30T08:18:55Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | We present an approach using deep reinforcement learning (DRL) to directly generate motion matching queries for longterm tasks, particularly targeting the reaching of specific locations. By integrating motion matching and DRL, our method demonstrates the rapid learning of policies for target location tasks within minutes on a standard desktop, employing a simple reward design. Additionally, we propose a unique hit reward and obstacle curriculum scheme to enhance policy learning in environments with moving obstacles. | en_US |
| dc.description.sectionheaders | Animation | |
| dc.description.seriesinformation | Eurographics 2024 - Short Papers | |
| dc.identifier.doi | 10.2312/egs.20241020 | |
| dc.identifier.isbn | 978-3-03868-237-0 | |
| dc.identifier.issn | 1017-4656 | |
| dc.identifier.pages | 4 pages | |
| dc.identifier.uri | https://doi.org/10.2312/egs.20241020 | |
| dc.identifier.uri | https://diglib.eg.org/handle/10.2312/egs20241020 | |
| dc.publisher | The Eurographics Association | en_US |
| dc.rights | Attribution 4.0 International License | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | CCS Concepts: Computing methodologies → Motion processing; Motion path planning | |
| dc.subject | Computing methodologies → Motion processing | |
| dc.subject | Motion path planning | |
| dc.title | Utilizing Motion Matching with Deep Reinforcement Learning for Target Location Tasks | en_US |