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Learning Safety Constraints From Demonstration Using One-Class Decision Trees
The alignment of autonomous agents with human values is a pivotal challenge when deploying these agents within physical environments, where safety is an important concern.
Maximum Causal Entropy Inverse Constrained Reinforcement Learning
To address this challenge, we propose a novel method that utilizes the principle of maximum causal entropy to learn constraints and an optimal policy that adheres to these constraints, using demonstrations of agents that abide by the constraints.
Intelligent Frame Selection as a Privacy-Friendlier Alternative to Face Recognition
For this, we train a variational autoencoder on high quality face images from a publicly available dataset and use the reconstruction probability as a metric to estimate the quality of each face crop.
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