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arXiv · 2103.04446

A Lower Bound for the Sample Complexity of Inverse Reinforcement Learning

Abi Komanduru·Jean Honorio
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Abstract

Inverse reinforcement learning (IRL) is the task of finding a reward function that generates a desired optimal policy for a given Markov Decision Process (MDP). This paper develops an information-theoretic lower bound for the sample complexity of the finite state, finite action IRL problem. A geometric construction of $\beta$-strict separable IRL problems using spherical codes is considered. Properties of the ensemble size as well as the Kullback-Leibler divergence between the generated trajectories are derived. The resulting ensemble is then used along with Fano's inequality to derive a sample complexity lower bound of $O(n \log n)$, where $n$ is the number of states in the MDP.

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Abi Komanduru, Jean Honorio. 2021-03-07. A Lower Bound for the Sample Complexity of Inverse Reinforcement Learning. https://arxiv.org/abs/2103.04446

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