Correlated Learning for Aggregation Systems

Aggregation systems (e. g. , Uber, Lyft, Food- Panda, 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 sup- ply and assigns them to demand so as to optimize a central metric such as profit, number of requests, delay etc. Due to optimizing a metric of importance to the centralized entity, the interests of individuals (e. g. , drivers, de- livery 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 in the presence of a self interested central entity. Since there are large number of learning agents that are homogenous, we represent the problem as an Anonymous Multi-Agent Reinforcement Learn- ing (AyMARL) problem. By using the self interested centralized entity as a correlation entity, we provide a novel learning mechanism that helps individual agents to maximize their individual revenue. Our Correlated Learning (CL) algorithm is able to outperform existing mechanisms on a generic simulator for aggregation systems and multiple other benchmark Multi-Agent Reinforcement Learning (MARL) problems.

Authors

• Tanvi Verma
• Pradeep Varakantham

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