Optimal Rate of Convergence for Quasi-Stochastic Approximation

TL;DR

This paper develops a deterministic quasi-stochastic approximation framework that reduces variance and achieves optimal convergence rates, with applications to reinforcement learning in deterministic environments.

Contribution

It introduces a new coupling argument for optimal convergence in quasi-stochastic approximation and applies it to RL algorithms for deterministic models.

Findings

Variance reduction is significant with proper parameter tuning.

Optimal convergence rates are established for linear models.

Demonstrated effectiveness in non-ideal and deterministic RL settings.

Abstract

The Robbins-Monro stochastic approximation algorithm is a foundation of many algorithmic frameworks for reinforcement learning (RL), and often an efficient approach to solving (or approximating the solution to) complex optimal control problems. However, in many cases practitioners are unable to apply these techniques because of an inherent high variance. This paper aims to provide a general foundation for "quasi-stochastic approximation," in which all of the processes under consideration are deterministic, much like quasi-Monte-Carlo for variance reduction in simulation. The variance reduction can be substantial, subject to tuning of pertinent parameters in the algorithm. This paper introduces a new coupling argument to establish optimal rate of convergence provided the gain is sufficiently large. These results are established for linear models, and tested also in non-ideal settings. A major application of these general results is a new class of RL algorithms for deterministic state space models. In this setting, the main contribution is a class of algorithms for approximating the value function for a given policy, using a different policy designed to introduce exploration.