Increasing Entropy to Boost Policy Gradient Performance on Personalization Tasks

TL;DR

This paper explores how regularization techniques that promote diversity in policy actions can enhance reinforcement learning performance on personalization tasks, demonstrating improved results across multiple datasets.

Contribution

It introduces a novel regularization method using $\

Findings

Enhanced policy diversity improves personalization performance.

Regularization increases policy effectiveness without accuracy loss.

Method effective across multiple datasets.

Abstract

In this effort, we consider the impact of regularization on the diversity of actions taken by policies generated from reinforcement learning agents trained using a policy gradient. Policy gradient agents are prone to entropy collapse, which means certain actions are seldomly, if ever, selected. We augment the optimization objective function for the policy with terms constructed from various $\varphi$-divergences and Maximum Mean Discrepancy which encourages current policies to follow different state visitation and/or action choice distribution than previously computed policies. We provide numerical experiments using MNIST, CIFAR10, and Spotify datasets. The results demonstrate the advantage of diversity-promoting policy regularization and that its use on gradient-based approaches have significantly improved performance on a variety of personalization tasks. Furthermore, numerical evidence is given to show that policy regularization increases performance without losing accuracy.