Towards Differentially Private Reinforcement Learning with General Function Approximation
arXiv:2605.07049v1 Announce Type: new
Abstract: We present the first theoretical guarantees for differentially private online reinforcement learning (RL) with general function approximation, extending beyond prior work restricted to tabular and linear…