Article
Learning incentivization strategy for resource rebalancing in shared services with a budget constraint
Journal of Applied and Numerical Optimization
(2021)
Abstract
In this paper, we describe the problem of learning an optimal incentivization strategy that maximizes the service level given a fixed budget constraint for a sharing service such as bike-sharing, car- sharing, etc. in a spatiotemporal environment. The service level can be affected due to an imbalance in supply and demand at different locations during a specific time period. We describe and present our study and comparison of various reinforcement learning algorithms on a 1-D problem setting in a simulated bike-share system with a budget constraint on the incentives. We empirically study the performance of three policy gradient based reinforcement learning algorithms, namely: Proximal Policy Optimization (PPO), Trust Region Policy Optimization (TRPO), and Actor Critic using Kronecker-Factored Trust Region (ACKTR).
Disciplines
Publication Date
April 1, 2021
DOI
10.23952/JANO.3.2021.1.07
Citation Information
Shen Shyang Ho, Matthew Schofield and Ning Wang. "Learning incentivization strategy for resource rebalancing in shared services with a budget constraint" Journal of Applied and Numerical Optimization Vol. 3 Iss. 1 (2021) p. 105 - 114 Available at: http://works.bepress.com/shen-shyang-ho/18/
Creative Commons license
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