Off-Policy Evaluation for Episodic Partially Observable Markov Decision Processes under Non-Parametric Models

Rui Miao · Zhengling Qi · Xiaoke Zhang

Hall J #729

Keywords: [ Reinforcement Learning Theory ] [ Off-Policy Evaluation ] [ Ill-posedness ] [ Non-parametric Instrumental Variable ]

[ Abstract ]
[ Paper [ OpenReview
Tue 29 Nov 2 p.m. PST — 4 p.m. PST


We study the problem of off-policy evaluation (OPE) for episodic Partially Observable Markov Decision Processes (POMDPs) with continuous states. Motivated by the recently proposed proximal causal inference framework, we develop a non-parametric identification result for estimating the policy value via a sequence of so-called V-bridge functions with the help of time-dependent proxy variables. We then develop a fitted-Q-evaluation-type algorithm to estimate V-bridge functions recursively, where a non-parametric instrumental variable (NPIV) problem is solved at each step. By analyzing this challenging sequential NPIV estimation, we establish the finite-sample error bounds for estimating the V-bridge functions and accordingly that for evaluating the policy value, in terms of the sample size, length of horizon and so-called (local) measure of ill-posedness at each step. To the best of our knowledge, this is the first finite-sample error bound for OPE in POMDPs under non-parametric models.

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