Quasi-Newton policy gradient algorithms

Haoya Li, Samarth Gupta, Hsiang-Fu Yu, Lexing Ying, Inderjit S Dhillon

Abstract: Policy gradient algorithms have been widely applied to reinforcement learning (RL) problems in recent years. Regularization with various entropy functions is often used to encourage exploration and improve stability. In this paper, we propose a quasi-Newton method for the policy gradient algorithm with entropy regularization. In the case of Shannon entropy, the resulting algorithm reproduces the natural policy gradient (NPG) algorithm. For other entropy functions, this method results in brand new policy gradient algorithms. We provide a simple proof that all these algorithms enjoy the Newton-type quadratic convergence near the optimal policy. Using synthetic and industrial-scale examples, we demonstrate that the proposed quasi-Newton method typically converges in single-digit iterations, often orders of magnitude faster than other state-of-the-art algorithms.

One-sentence Summary: We proposed a quasi-Newton policy gradient algorithm for reinforcement learning problems with general entropic regularization and proved its quadratic convergence.