Kyohei Suzuki

Non-Convex Sparse Reinforcement Learning via Non-Monotone Inclusions

Kyohei Suzuki, Konstantinos Slavakis

This work delivers two key contributions: one to efficient feature selection in reinforcement learning (RL), the other to the theory of non-monotone inclusions. On the RL side, the estimation bias inherent in conventional regularization schemes is addressed by augmenting classica…Read more