NeurIPS 2019
Sun Dec 8th through Sat the 14th, 2019 at Vancouver Convention Center
Paper ID:4404
Title:Provably Efficient Q-learning with Function Approximation via Distribution Shift Error Checking Oracle

The paper proposes an adaptation of the classical Q-learning algorithm with linear function approximation that enjoys polynomial sample complexity. All reviewers feel the paper contains interesting contribution to the RL literature that should appear in this conference, and I therefore recommend acceptance.