Correlated Learning for Aggregation Systems

Tanvi Verma, Pradeep Varakantham
Proceedings of The 35th Uncertainty in Artificial Intelligence Conference, PMLR 115:60-70, 2020.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v115-verma20a, title = {Correlated Learning for Aggregation Systems}, author = {Verma, Tanvi and Varakantham, Pradeep}, booktitle = {Proceedings of The 35th Uncertainty in Artificial Intelligence Conference}, pages = {60--70}, year = {2020}, editor = {Adams, Ryan P. and Gogate, Vibhav}, volume = {115}, series = {Proceedings of Machine Learning Research}, month = {22--25 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v115/verma20a/verma20a.pdf}, url = {https://proceedings.mlr.press/v115/verma20a.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T Correlated Learning for Aggregation Systems %A Tanvi Verma %A Pradeep Varakantham %B Proceedings of The 35th Uncertainty in Artificial Intelligence Conference %C Proceedings of Machine Learning Research %D 2020 %E Ryan P. Adams %E Vibhav Gogate %F pmlr-v115-verma20a %I PMLR %P 60--70 %U https://proceedings.mlr.press/v115/verma20a.html %V 115 %X 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.
APA
Verma, T. & Varakantham, P.. (2020). Correlated Learning for Aggregation Systems. Proceedings of The 35th Uncertainty in Artificial Intelligence Conference, in Proceedings of Machine Learning Research 115:60-70 Available from https://proceedings.mlr.press/v115/verma20a.html.

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