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

Understand

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.
• Existing studies that guarantee differential privacy are not extendable to infinite state spaces, as the noise level to ensure privacy will scale accordingly to infinity.
• Our aim is to protect the value function approximator, without regard to the number of states queried to the function.