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

Additional URL

https://proceedings.mlr.press/v180/ghosh22a.html

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