PhD Dissertation Defense by Tanvi VERMA | Scalable Multi-Agent Reinforcement Learning for Aggregation Systems

About The Talk

Efficient sequential matching of supply and demand is a problem of interest in many online to offline services. For instance, Uber, Lyft, Grab for matching taxis to cus tomers; Ubereats, Deliveroo, FoodPanda etc. for matching restaurants to customers. In these systems, a centralized entity (e.g., Uber) aggregates supply and assigns them to demand so as to optimize a central metric such as profit, number of requests, delay etc. However, individuals (e.g., drivers, delivery boys) in the system are self-interested and they try to maximize their own long term profit. The central entity has the full view of the system and it can learn policies to maximize the overall payoff and suggest it to the individuals. However, due to the selfish nature of the individuals, they might not be interested in following the suggestion. Hence, in my work, I have focused of developing approaches such that the system is in equilibrium and the overall performance of the system is improved. In this talk, I will explain the different learning approaches which can be used by the individuals in the presence of a self-interested centralized entity such that their long term revenue is maximized.

Speaker Biography

Tanvi VERMA is a PhD candidate in School of Information Systems, Singapore Management University. She is part of Intelligent Systems and Optimization Group and is advised by Associate Professor Pradeep Varakantham and co-advised by Professor Hoong Chuin Lau. She received her B.Tech in Computer Science & Engineering from National Institute of Technology (NIT), Warangal, India. She then worked as a software engineer at NetApp, Bangalore before joining the PhD program at SMU in 2015. Her key research interests include Decision Making under Uncertainty, Multi Agent Reinforcement Learning and Game Theory.