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Correlated Fictitious Play for Aggregation Systems
Speaker (s):
Tanvi VERMA
PhD Candidate
School of Information Systems
Singapore Management University |
Date:
Time:
Venue:
| | July 8, 2019, Monday
2:00pm - 2.20pm
Meeting Room 4.4, Level 4
School of Information Systems
Singapore Management University
80 Stamford Road
Singapore 178902
We look forward to seeing you at this research seminar.
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About the Talk
Aggregation systems (e.g., Uber, Lyft, FoodPanda, Deliveroo) have been increasingly used to improve efficiency in numerous environments, including in transportation, logistics, food and grocery delivery. In these systems, a centralized entity (e.g., Uber) aggregates supply and assigns them to demand so as to optimize a central metric (e.g, profit, number of requests, delay). Due to optimizing a metric of importance to the centralized entity, the interests of individuals (e.g., drivers, delivery boys) can be sacrificed. Therefore, in this paper, we focus on the problem of serving individual interests, i.e., learning revenue maximizing policies for individuals.
Since there are large number of learning agents that are homogenous and a centralized entity, we represent the problem as an Anonymous Multi-Agent Reinforcement Learning (AyMARL) problem. By using the centralized entity as a correlation entity (that is trying to maximize its own revenue by recommending best actions to individual supply units), we provide a novel fictitious play mechanism that helps individual agents to maximize their individual revenue. Our Correlated Fictitious Play (CFP) algorithm is able to outperform existing mechanisms on a generic simulator for aggregation systems and multiple other benchmark Multi Agent Reinforcement Learning (MARL) problems.
This is a pre-conference talk for Conference on Uncertainty in Artificial Intelligence.
About the Speaker
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. 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, Reinforcement Learning, Game Theory and Multiagent Systems.
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