Estimating Risk and Uncertainty in Deep Reinforcement Learning

May 23, 2019 ยท Declared Dead ยท ๐Ÿ› arXiv.org

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Authors William R. Clements, Bastien Van Delft, Benoรฎt-Marie Robaglia, Reda Bahi Slaoui, Sรฉbastien Toth arXiv ID 1905.09638 Category cs.LG: Machine Learning Cross-listed cs.AI, stat.ML Citations 112 Venue arXiv.org Last Checked 4 months ago
Abstract
Reinforcement learning agents are faced with two types of uncertainty. Epistemic uncertainty stems from limited data and is useful for exploration, whereas aleatoric uncertainty arises from stochastic environments and must be accounted for in risk-sensitive applications. We highlight the challenges involved in simultaneously estimating both of them, and propose a framework for disentangling and estimating these uncertainties on learned Q-values. We derive unbiased estimators of these uncertainties and introduce an uncertainty-aware DQN algorithm, which we show exhibits safe learning behavior and outperforms other DQN variants on the MinAtar testbed.
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