Constrained Model-Free Reinforcement Learning for Process Optimization

TL;DR

This paper introduces a constrained Q-learning algorithm that guarantees high-probability satisfaction of safety constraints, advancing reinforcement learning's applicability to real-world process control.

Contribution

It proposes an oracle-assisted, self-tuning constraint tightening method integrated into RL to ensure safety constraints are met with high probability in process optimization.

Findings

Algorithm guarantees constraint satisfaction with high probability

Performance comparable or superior to model predictive control

Applicable to safety-critical industrial processes

Abstract

Reinforcement learning (RL) is a control approach that can handle nonlinear stochastic optimal control problems. However, despite the promise exhibited, RL has yet to see marked translation to industrial practice primarily due to its inability to satisfy state constraints. In this work we aim to address this challenge. We propose an 'oracle'-assisted constrained Q-learning algorithm that guarantees the satisfaction of joint chance constraints with a high probability, which is crucial for safety critical tasks. To achieve this, constraint tightening (backoffs) are introduced and adjusted using Broyden's method, hence making them self-tuned. This results in a general methodology that can be imbued into approximate dynamic programming-based algorithms to ensure constraint satisfaction with high probability. Finally, we present case studies that analyze the performance of the proposed approach and compare this algorithm with model predictive control (MPC). The favorable performance of this algorithm signifies a step toward the incorporation of RL into real world optimization and control of engineering systems, where constraints are essential in ensuring safety.