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Average-Reward Off-Policy Policy Evaluation with Function Approximation

2021-01-08 00:43:04
Shangtong Zhang, Yi Wan, Richard S. Sutton, Shimon Whiteson

Abstract

tract: We consider off-policy policy evaluation with function approximation (FA) in average-reward MDPs, where the goal is to estimate both the reward rate and the differential value function. For this problem, bootstrapping is necessary and, along with off-policy learning and FA, results in the deadly triad (Sutton & Barto, 2018). To address the deadly triad, we propose two novel algorithms, reproducing the celebrated success of Gradient TD algorithms in the average-reward setting. In terms of estimating the differential value function, the algorithms are the first convergent off-policy linear function approximation algorithms. In terms of estimating the reward rate, the algorithms are the first convergent off-policy linear function approximation algorithms that do not require estimating the density ratio. We demonstrate empirically the advantage of the proposed algorithms, as well as their nonlinear variants, over a competitive density-ratio-based approach, in a simple domain as well as challenging robot simulation tasks.

Abstract (translated)

URL

https://arxiv.org/abs/2101.02808

PDF

https://arxiv.org/pdf/2101.02808


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