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Uncertainty-Based Out-of-Distribution Classification in Deep Reinforcement Learning
Robustness to out-of-distribution (OOD) data is an important goal in building
reliable machine learning systems. Especially in autonomous systems, wrong
predictions for OOD inputs can cause safety critical situations. As a first
step towards a solution, we consider the problem of detecting such data in a
value-based deep reinforcement learning (RL) setting. Modelling this problem as
a one-class classification problem, we propose a framework for
uncertainty-based OOD classification: UBOOD. It is based on the effect that an
agent's epistemic uncertainty is reduced for situations encountered during
training (in-distribution), and thus lower than for unencountered (OOD)
situations. Being agnostic towards the approach used for estimating epistemic
uncertainty, combinations with different uncertainty estimation methods, e.g.
approximate Bayesian inference methods or ensembling techniques are possible.
We further present a first viable solution for calculating a dynamic
classification threshold, based on the uncertainty distribution of the training
data. Evaluation shows that the framework produces reliable classification
results when combined with ensemble-based estimators, while the combination
with concrete dropout-based estimators fails to reliably detect OOD situations.
In summary, UBOOD presents a viable approach for OOD classification in deep RL
settings by leveraging the epistemic uncertainty of the agent's value function.Comment: arXiv admin note: text overlap with arXiv:1901.0221
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