On Convergence of some Gradient-based Temporal-Differences Algorithms for Off-Policy Learning

TL;DR

This paper provides comprehensive convergence analysis for various gradient-based off-policy TD algorithms with linear function approximation, covering different stepsize strategies and lambda-parameter schemes.

Contribution

It introduces new convergence results for multiple variants of gradient-based TD algorithms, including their robustified, mirror-descent, and single-time-scale forms, under diverse conditions.

Findings

Convergence established for constant, diminishing, and square-summable stepsizes.

Analysis covers state-dependent, history-dependent, and combined lambda schemes.

Results include almost sure convergence and asymptotic behavior characterizations.

Abstract

We consider off-policy temporal-difference (TD) learning methods for policy evaluation in Markov decision processes with finite spaces and discounted reward criteria, and we present a collection of convergence results for several gradient-based TD algorithms with linear function approximation. The algorithms we analyze include: (i) two basic forms of two-time-scale gradient-based TD algorithms, which we call GTD and which minimize the mean squared projected Bellman error using stochastic gradient-descent; (ii) their "robustified" biased variants; (iii) their mirror-descent versions which combine the mirror-descent idea with TD learning; and (iv) a single-time-scale version of GTD that solves minimax problems formulated for approximate policy evaluation. We derive convergence results for three types of stepsizes: constant stepsize, slowly diminishing stepsize, as well as the standard type of diminishing stepsize with a square-summable condition. For the first two types of stepsizes, we apply the weak convergence method from stochastic approximation theory to characterize the asymptotic behavior of the algorithms, and for the standard type of stepsize, we analyze the algorithmic behavior with respect to a stronger mode of convergence, almost sure convergence. Our convergence results are for the aforementioned TD algorithms with three general ways of setting their $\lambda$-parameters: (i) state-dependent $\lambda$; (ii) a recently proposed scheme of using history-dependent $\lambda$ to keep the eligibility traces of the algorithms bounded while allowing for relatively large values of $\lambda$; and (iii) a composite scheme of setting the $\lambda$-parameters that combines the preceding two schemes and allows a broader class of generalized Bellman operators to be used for approximate policy evaluation with TD methods.