Damped Anderson Mixing for Deep Reinforcement Learning: Acceleration, Convergence, and Stabilization

TL;DR

This paper provides a rigorous analysis of Anderson mixing in deep reinforcement learning, demonstrating its benefits for acceleration, convergence, and stability through theoretical insights and extensive experiments.

Contribution

It establishes a theoretical connection between Anderson mixing and quasi-Newton methods, and introduces stabilization strategies for improved deep RL performance.

Findings

Increases convergence radius of policy iteration schemes.

Enhances stability and performance of RL algorithms.

Provides a theoretical foundation for Anderson mixing in RL.

Abstract

Anderson mixing has been heuristically applied to reinforcement learning (RL) algorithms for accelerating convergence and improving the sampling efficiency of deep RL. Despite its heuristic improvement of convergence, a rigorous mathematical justification for the benefits of Anderson mixing in RL has not yet been put forward. In this paper, we provide deeper insights into a class of acceleration schemes built on Anderson mixing that improve the convergence of deep RL algorithms. Our main results establish a connection between Anderson mixing and quasi-Newton methods and prove that Anderson mixing increases the convergence radius of policy iteration schemes by an extra contraction factor. The key focus of the analysis roots in the fixed-point iteration nature of RL. We further propose a stabilization strategy by introducing a stable regularization term in Anderson mixing and a differentiable, non-expansive MellowMax operator that can allow both faster convergence and more stable behavior. Extensive experiments demonstrate that our proposed method enhances the convergence, stability, and performance of RL algorithms.