Preventing Value Function Collapse in Ensemble {Q}-Learning by Maximizing Representation Diversity
This addresses a key limitation in reinforcement learning for improving value estimation in DQN-based methods, though it appears incremental as it builds on existing ensemble techniques.
The paper tackles the problem of value function collapse in ensemble Q-learning algorithms by introducing a regularization technique to maximize ensemble diversity, showing that this approach significantly outperforms both ensemble and non-ensemble baselines.
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.