# Actor Critic with Differentially Private Critic

arXiv: https://arxiv.org/abs/1910.05876 · HTML: https://mlsanity.com/paper/1910.05876/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2019.

## Built on by (most cited first, 5 of 5)

- [Offline Reinforcement Learning with Differential Privacy](https://mlsanity.com/paper/2206.00810/) (2022)
- [Differentially Private Temporal Difference Learning with Stochastic Nonconvex-Strongly-Concave Optimization](https://mlsanity.com/paper/2201.10447/) (2022)
- [How Private Is Your RL Policy? An Inverse RL Based Analysis Framework](https://mlsanity.com/paper/2112.05495/) (2021)
- [Reinforcement Learning on Encrypted Data](https://mlsanity.com/paper/2109.08236/) (2021)
- [Differentially Private Reinforcement Learning with Self-Play](https://mlsanity.com/paper/2404.07559/) (2024)

## Abstract (arXiv)

Reinforcement learning algorithms are known to be sample inefficient, and often performance on one task can be substantially improved by leveraging information (e.g., via pre-training) on other related tasks. In this work, we propose a technique to achieve such knowledge transfer in cases where agent trajectories contain sensitive or private information, such as in the healthcare domain. Our approach leverages a differentially private policy evaluation algorithm to initialize an actor-critic model and improve the effectiveness of learning in downstream tasks. We empirically show this technique increases sample efficiency in resource-constrained control problems while preserving the privacy of trajectories collected in an upstream task.