13 research outputs found
Learning to Identify Critical States for Reinforcement Learning from Videos
Recent work on deep reinforcement learning (DRL) has pointed out that
algorithmic information about good policies can be extracted from offline data
which lack explicit information about executed actions. For example, videos of
humans or robots may convey a lot of implicit information about rewarding
action sequences, but a DRL machine that wants to profit from watching such
videos must first learn by itself to identify and recognize relevant
states/actions/rewards. Without relying on ground-truth annotations, our new
method called Deep State Identifier learns to predict returns from episodes
encoded as videos. Then it uses a kind of mask-based sensitivity analysis to
extract/identify important critical states. Extensive experiments showcase our
method's potential for understanding and improving agent behavior. The source
code and the generated datasets are available at
https://github.com/AI-Initiative-KAUST/VideoRLCS.Comment: This paper was accepted to ICCV2
Explanation Uncertainty with Decision Boundary Awareness
Post-hoc explanation methods have become increasingly depended upon for
understanding black-box classifiers in high-stakes applications, precipitating
a need for reliable explanations. While numerous explanation methods have been
proposed, recent works have shown that many existing methods can be
inconsistent or unstable. In addition, high-performing classifiers are often
highly nonlinear and can exhibit complex behavior around the decision boundary,
leading to brittle or misleading local explanations. Therefore, there is an
impending need to quantify the uncertainty of such explanation methods in order
to understand when explanations are trustworthy. We introduce a novel
uncertainty quantification method parameterized by a Gaussian Process model,
which combines the uncertainty approximation of existing methods with a novel
geodesic-based similarity which captures the complexity of the target black-box
decision boundary. The proposed framework is highly flexible; it can be used
with any black-box classifier and feature attribution method to amortize
uncertainty estimates for explanations. We show theoretically that our proposed
geodesic-based kernel similarity increases with the complexity of the decision
boundary. Empirical results on multiple tabular and image datasets show that
our decision boundary-aware uncertainty estimate improves understanding of
explanations as compared to existing methods