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Differentially Private Reinforcement Learning with Linear Function Approximation
2022
Abstract Proceedings of the 2022 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems
Motivated by the wide adoption of reinforcement learning (RL) in real-world personalized services , where users' sensitive and private information needs to be protected, we study regret minimization in finite-horizon Markov decision processes (MDPs) under the constraints of differential privacy (DP). Compared to existing private RL algorithms that work only on tabular finite-state, finiteactions MDPs, we take the first step towards privacy-preserving learning in MDPs with large state and action
doi:10.1145/3489048.3522648
fatcat:pq4hpup2ovc4zhkwji3ve44qr4