AI RESEARCH
Randomized Least Squares Value Iteration itself is Joint Differentially Private
arXiv CS.LG
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ArXi:2606.01952v1 Announce Type: new As reinforcement learning (RL) increasingly applies to sensitive domains, such as health care and recommendation systems, privacy-preserving techniques have become essential to protect users' sensitive information. We investigate privacy-preserving RL under an episodic setting, focusing on algorithms based on randomized exploration, such as Randomized Least Squares Value Iteration (RLSVI). The overall goal is to study how randomized exploration interacts with the injected noise required by privacy mechanisms.