Neural-Progressive Hedging: Enforcing Constraints in Reinforcement Learning with Stochastic Programming
This addresses the challenge of scalable feasibility in sequential resource allocation problems for domains like finance and logistics, offering an incremental improvement with modest overhead.
The paper tackles the problem of enforcing constraints and risk-based objectives in reinforcement learning policies during online execution, proposing a neural-progressive hedging framework that leverages stochastic programming. Experimental results show it produces better policies than deep RL and baselines on real-world tasks like portfolio optimization and bike sharing, adapting to non-stationarity while satisfying constraints.
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.