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arXiv · 2008.04990

Batch Value-function Approximation with Only Realizability

Tengyang Xie·Nan Jiang
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Abstract

We make progress in a long-standing problem of batch reinforcement learning (RL): learning $Q^\star$ from an exploratory and polynomial-sized dataset, using a realizable and otherwise arbitrary function class. In fact, all existing algorithms demand function-approximation assumptions stronger than realizability, and the mounting negative evidence has led to a conjecture that sample-efficient learning is impossible in this setting (Chen and Jiang, 2019). Our algorithm, BVFT, breaks the hardness conjecture (albeit under a stronger notion of exploratory data) via a tournament procedure that reduces the learning problem to pairwise comparison, and solves the latter with the help of a state-action partition constructed from the compared functions. We also discuss how BVFT can be applied to model selection among other extensions and open problems.

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Tengyang Xie, Nan Jiang. 2021-06-17. Batch Value-function Approximation with Only Realizability. https://arxiv.org/abs/2008.04990

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