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Events for November 19, 2024
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PhD Thesis Proposal - Lixing Liu
Tue, Nov 19, 2024 @ 12:30 PM - 02:00 PM
Thomas Lord Department of Computer Science
University Calendar
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/8634079147Location: Institute For Creative Technologies (ICT) - 202
Audiences: Everyone Is Invited
Contact: Lixing Liu
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor.
