However,extrinsic rewards may be insufficiently informative to encourage an agent to explore and understand its environment, particularly in partially observed settings where the agent has a limited view of its environment.

A major challenge is thus in designing sample-efficient agents that can transfer their existing knowledge to solve new tasks quickly.

We formulate this notion of privacy for RL by leveraging the local differential privacy(LDP)framework.

Deep learning algorithms have shown significant development thanks tothelargepre-collected dataset, such as SQuAD [47]innatural language processing (NLP), and ImageNet [4] in computer vision.