Privacy-preserving Q-Learning with Functional Noise in Continuous State Spaces

30 Jan 2019 Baoxiang Wang Nidhi Hegde

We consider differentially private algorithms for reinforcement learning in continuous spaces, such that neighboring reward functions are indistinguishable. This protects the reward information from being exploited by methods such as inverse reinforcement learning... (read more)

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