Logistic Q-Learning

TL;DR

The paper introduces QREPS, a new model-free reinforcement learning algorithm that uses a convex loss function for policy evaluation, enabling efficient optimization and demonstrating strong performance on benchmark problems.

Contribution

It presents QREPS, a novel reinforcement learning algorithm that combines a convex loss for policy evaluation with an exact model-free implementation.

Findings

Effective on benchmark problems

Convex loss improves policy evaluation

Error analysis links updates to policy quality

Abstract

We propose a new reinforcement learning algorithm derived from a regularized linear-programming formulation of optimal control in MDPs. The method is closely related to the classic Relative Entropy Policy Search (REPS) algorithm of Peters et al. (2010), with the key difference that our method introduces a Q-function that enables efficient exact model-free implementation. The main feature of our algorithm (called QREPS) is a convex loss function for policy evaluation that serves as a theoretically sound alternative to the widely used squared Bellman error. We provide a practical saddle-point optimization method for minimizing this loss function and provide an error-propagation analysis that relates the quality of the individual updates to the performance of the output policy. Finally, we demonstrate the effectiveness of our method on a range of benchmark problems.