Multi-agent footstep steering with deep reinforcement learning
| dc.contributor.author | Peng, Kun | |
| dc.contributor.supervisor | Haworth, Brandon | |
| dc.date.accessioned | 2024-12-13T18:44:14Z | |
| dc.date.available | 2024-12-13T18:44:14Z | |
| dc.date.issued | 2024 | |
| dc.degree.department | Department of Computer Science | |
| dc.degree.level | Master of Science MSc | |
| dc.description.abstract | Crowd simulation plays a crucial role in a wide range of fields, from digital media to urban planning. However, traditional particle-based algorithms often lack essential information to present realistic human bipedal locomotion. This research aims to propose a more realistic and efficient steering model for crowd simulation by combining Multi-Agent Reinforcement Learning (MARL) with bipedal locomotion modelling. This study explores the advantages of MARL and analyzes a mathematical approach to simplifying complex bipedal locomotion. The approach utilizes the Proximal Policy Optimization algorithm and trains the model in adjustable randomized maze-like environments. Assessment results of the model indicate that the model learns goal-reaching behaviours and learns to avoid static and dynamic obstacles. Furthermore, the agents can simulate complex steering behaviours such as side-stepping and turning-like behaviours with two feet. This research contributes to the advancement of the field of crowd simulation through a flexible and realistic approach to modelling human steering behaviours in complex and dynamic environments. | |
| dc.description.scholarlevel | Graduate | |
| dc.identifier.uri | https://hdl.handle.net/1828/20840 | |
| dc.language | English | eng |
| dc.language.iso | en | |
| dc.rights | Available to the World Wide Web | |
| dc.subject | crowd simulation | |
| dc.subject | multi-agent reinforcement learning | |
| dc.subject | bipedal locomotion | |
| dc.title | Multi-agent footstep steering with deep reinforcement learning | |
| dc.type | Thesis |