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arXiv · 2101.02808

Average-Reward Off-Policy Policy Evaluation with Function Approximation

Shangtong Zhang·Yi Wan·Richard S. Sutton·Shimon Whiteson
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Abstract

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.

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Shangtong Zhang, Yi Wan, Richard S. Sutton, Shimon Whiteson. 2021-01-08. Average-Reward Off-Policy Policy Evaluation with Function Approximation. https://arxiv.org/abs/2101.02808

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