Damped Anderson Mixing for Deep Reinforcement Learning: Acceleration, Convergence, and Stabilization
This work addresses the problem of accelerating and stabilizing deep reinforcement learning algorithms, offering incremental improvements with theoretical backing.
The paper tackles the lack of rigorous justification for using Anderson mixing in reinforcement learning by connecting it to quasi-Newton methods and proving it increases the convergence radius of policy iteration, and proposes a stabilization strategy with a regularization term and MellowMax operator to improve convergence and stability.
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.