Machine Learning

Decomposition of Uncertainty for Active Learning and Reliable Reinforcement Learning in Stochastic Systems

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  • arXiv
    5 pts

    Decomposition of Uncertainty for Active Learning and Reliable Reinforcement Learning in Stochastic Systems

    Bayesian neural networks (BNNs) with latent variables are probabilistic models which can automatically identify complex stochastic patterns in the data. We study in these models a decomposition of predictive uncertainty into its epistemic and aleatoric components. We show how such a decomposition arises naturally in a Bayesian active learning scenario and develop a new objective for reliable reinforcement learning (RL) with an epistemic and aleatoric risk element. Our experiments illustrate the usefulness of the resulting decomposition in active learning and reliable RL.

    Decomposition of Uncertainty for Active Learning and Reliable Reinforcement Learning in Stochastic Systems
    by Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, Steffen Udluft
    https://arxiv.org/pdf/1710.07283v1.pdf

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