Increasing Entropy to Boost Policy Gradient Performance on Personalization Tasks
This addresses the problem of limited action diversity in reinforcement learning agents for personalization tasks, representing an incremental improvement through regularization techniques.
The paper tackles the problem of entropy collapse in policy gradient reinforcement learning by augmenting the optimization objective with diversity-promoting regularization terms based on φ-divergences and Maximum Mean Discrepancy. The results show significantly improved performance on personalization tasks using MNIST, CIFAR10, and Spotify datasets without losing accuracy.
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.