Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
8-2022
Abstract
We propose a framework, called neural-progressive hedging (NP), that leverages stochastic programming during the online phase of executing a reinforcement learning (RL) policy. The goal is to ensure feasibility with respect to constraints and risk-based objectives such as conditional value-at-risk (CVaR) during the execution of the policy, using probabilistic models of the state transitions to guide policy adjustments. The framework is particularly amenable to the class of sequential resource allocation problems since feasibility with respect to typical resource constraints cannot be enforced in a scalable manner. The NP framework provides an alternative that adds modest overhead during the online phase. Experimental results demonstrate the efficacy of the NP framework on two continuous real-world tasks: (i) the portfolio optimization problem with liquidity constraints for financial planning, characterized by non-stationary state distributions; and (ii) the dynamic repositioning problem in bike sharing systems, that embodies the class of supply-demand matching problems. We show that the NP framework produces policies that are better than deep RL and other baseline approaches, adapting to non-stationarity, whilst satisfying structural constraints and accommodating risk measures in the resulting policies. Additional benefits of the NP framework are ease of implementation and better explainability of the policies.
Keywords
Financial data processing, Financial markets, Risk assessment, Stochastic programming, Stochastic systems, Value engineering
Discipline
Finance and Financial Management | Theory and Algorithms
Publication
UAI 2022: Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, Eindhoven, August 1-5
First Page
707
Last Page
717
ISBN
9781713863298
Publisher
Association for Uncertainty in Artificial Intelligence (AUAI)
City or Country
Eindhoven
Citation
GHOSH, Supriyo; WYNTER, Laura; LIM, Shiau Hong; and NGUYEN, Duc Thien.
Neural-progressive hedging: Enforcing constraints in reinforcement learning with stochastic programming. (2022). UAI 2022: Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, Eindhoven, August 1-5. 707-717.
Available at: https://ink.library.smu.edu.sg/sis_research/7760
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://proceedings.mlr.press/v180/ghosh22a.html