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Uncertainty-Based Out-of-Distribution Detection in Deep Reinforcement Learning
[article]
2019
arXiv
pre-print
We consider the problem of detecting out-of-distribution (OOD) samples in deep reinforcement learning. In a value based reinforcement learning setting, we propose to use uncertainty estimation techniques directly on the agent's value estimating neural network to detect OOD samples. The focus of our work lies in analyzing the suitability of approximate Bayesian inference methods and related ensembling techniques that generate uncertainty estimates. Although prior work has shown that
arXiv:1901.02219v1
fatcat:j6pxwgzknjakdg3hn77ykrv5wq