007, Distributed Learning and Inference

Abstract:

While notable  progress has been made in specifying objectives for general cyber-physical systems, applying these methods to distributed systems still pose significant challenges.  Among these are the need to (a) craft specification primitives that allow expression and interplay of both local and global objectives, (b) tame explosion in the state and action spaces to enable effective learning, and (c) minimize coordination frequency and the set of engaged participants for global objectives.  To address these challenges, we propose a novel specification framework that allows natural composition of local and global objectives used to guide training of a multi-agent system. Our technique enables learning expressive policies that allow agents to operate in a coordination-free manner for local objectives, using a decentralized communication protocol for enforcing global ones. Experimental results support our claim that sophisticated multi-agent distributed planning problems can be effectively realized using specification-guided learning.

 

Bio:

Joe Kurian Eappen is a graduate student in the School of Electrical and Computer Engineering at Purdue University working under the supervision of Prof. Suresh Jagannathan. He completed his Dual Degree (B.Tech & M.Tech) in Electrical Engineering at the Indian Institute of Technology (IIT) Madras, India in 2018. His current research lies at the intersection of Reinforcement Learning, Multi-Agent systems and Verifiable Machine Learning.