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arXiv · 2011.01075

A Variant of the Wang-Foster-Kakade Lower Bound for the Discounted Setting

Philip Amortila·Nan Jiang·Tengyang Xie
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Abstract

Recently, Wang et al. (2020) showed a highly intriguing hardness result for batch reinforcement learning (RL) with linearly realizable value function and good feature coverage in the finite-horizon case. In this note we show that once adapted to the discounted setting, the construction can be simplified to a 2-state MDP with 1-dimensional features, such that learning is impossible even with an infinite amount of data.

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Philip Amortila, Nan Jiang, Tengyang Xie. 2020-11-02. A Variant of the Wang-Foster-Kakade Lower Bound for the Discounted Setting. https://arxiv.org/abs/2011.01075

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