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Topic

Multi-agent Footstep Steering with Deep Reinforcement Learning

Department of Mechanical Engineering

Date & location

• Tuesday, December 10, 2024
• 12:30 P.M.
• Engineering Computer Science Building
• Room 467

Reviewers

Supervisory Committee

• Dr. Brandon Haworth, Department of Computer Science, University of Victoria (Supervisor)

• Dr. Teseo Schnieder, Department of Computer Science, UVic (Member) 

External Examiner

• Dr. Homayoun Najjaran, Department of Mechanical Engineering, University of Washington 

Chair of Oral Examination

• Dr. Adam Krawitz, Department of Psychology, UVic

   

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 Pol icy 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.