PhD Thesis Proposal - Lixing Liu

Title: Leveraging Organizational Hierarchies for Goal Management in Multi-Agent Reinforcement Learning  
 
Date and Time: Nov. 19th, 2024 - 12:30p - 2:00p  
 
Location: ICT 202  
 
Committee Members: William Swartout (chair), Paul Rosenbloom, Kallirroi Georgila, Daniel O’Leary, Emilio Ferrara
 
Abstract: Effectively assigning credit and managing goals remain central challenges in Multi-Agent Reinforcement Learning (MARL), especially in stochastic environments with varying  agent priorities across decision levels. Inspired by organizational hierarchies, this study structures multi-agent systems at different levels of abstraction and coordination. It hypothesizes that integrating a structured goal management mechanism within a MARL pipeline can: 1) improve the performance of prioritized, long-horizon tactical behaviors, 2) enhance the transferability of short-term operational behaviors, and 3) accelerate learning for faster MARL behavior model development. The proposed framework employs a hierarchy-aligned, soft-constraint goal-splitting strategy tailored to each agent’s capabilities, planning horizon, and organizational role. Furthermore, it enhances the manageability and interpretability of learned behaviors by incorporating sparse external graph networks to model environmental and inter-agent dynamics. This framework provides a solution for hierarchical goal management in MARL, evaluated for performance, efficiency and team coordination within two-team cooperative-competitive simulations involving complex maneuvers and engagements.  
 
Zoom Link: https://usc.zoom.us/j/8634079147

Location: Institute For Creative Technologies (ICT) - 202

Audiences: Everyone Is Invited

Contact: Lixing Liu