adaPARL: Adaptive Privacy-Aware Reinforcement Learning for Sequential-Decision Making Human-in-the-Loop Systems. (arXiv:2303.04257v1 [cs.LG])

Reinforcement learning (RL) presents numerous benefits compared to rule-based
approaches in various applications. Privacy concerns have grown with the
widespread use of RL trained with privacy-sensitive data in IoT devices,
especially for human-in-the-loop systems. On the one hand, RL methods enhance
the user experience by trying to adapt to the highly dynamic nature of humans.
On the other hand, trained policies can leak the user's private information.
Recent attention has been drawn to designing privacy-aware RL algorithms while
maintaining an …

applications aware benefits data decision decision making devices dynamic experience human humans information iot iot devices leak loop making nature policies privacy sensitive data systems user experience