Exploration via Epistemic Value Estimation

Simon Schmitt; John Shawe-Taylor; Hado van Hasselt

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arXiv · 2303.04012

Exploration via Epistemic Value Estimation

Simon Schmitt·John Shawe-Taylor·Hado van Hasselt

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Abstract

How to efficiently explore in reinforcement learning is an open problem. Many exploration algorithms employ the epistemic uncertainty of their own value predictions -- for instance to compute an exploration bonus or upper confidence bound. Unfortunately the required uncertainty is difficult to estimate in general with function approximation. We propose epistemic value estimation (EVE): a recipe that is compatible with sequential decision making and with neural network function approximators. It equips agents with a tractable posterior over all their parameters from which epistemic value uncertainty can be computed efficiently. We use the recipe to derive an epistemic Q-Learning agent and observe competitive performance on a series of benchmarks. Experiments confirm that the EVE recipe facilitates efficient exploration in hard exploration tasks.

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cs.LG cs.AI stat.ML

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Simon Schmitt, John Shawe-Taylor, Hado van Hasselt. 2023-03-07. Exploration via Epistemic Value Estimation. https://arxiv.org/abs/2303.04012

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