Details

Autor(en) / Beteiligte

• Starnes, Andrew
• Dereventsov, Anton
• Webster, Clayton

Titel

Increasing Entropy to Boost Policy Gradient Performance on Personalization Tasks

Ist Teil von

• 2023 IEEE International Conference on Data Mining Workshops (ICDMW), 2023, p.1551-1558

Ort / Verlag

IEEE

Erscheinungsjahr

2023

Quelle

IEEE/IET Electronic Library (IEL)

Beschreibungen/Notizen

• 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 φ-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.