Preventing Value Function Collapse in Ensemble {Q}-Learning by Maximizing Representation Diversity
Year: 2020
Type: preprint
Abstract: The classic DQN algorithm is limited by the overestimation bias of the learned Q-function. Subsequent algorithms have proposed techniques to reduce this problem, without fully eliminating it. Recently, the Maxmin and Ensemble Q-learning algorithms have used different estimates provided by the ensembles of learners to reduce the overestimation bias. Unfortunately, these learners can converge to the same point in the parametric or representation space, falling back to the classic single neural network DQN. In this paper, we describe a regularization technique to maximize ensemble diversity in these algorithms. We propose and compare five regularization functions inspired from economics theory and consensus optimization. We show that the regularized approach significantly outperforms the Maxmin and Ensemble Q-learning algorithms as well as non-ensemble baselines.
Source: arXiv (Cornell University)
Authors: Sheikh, Hassam Ullah, Bölöni, Ladislau
Institutions: University of Central Florida, Intel (United States)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: National Science Foundation
OpenAlex ID: W3130711336