Deceptive Reinforcement Learning for Privacy-Preserving Planning
This work tackles the problem of protecting sensitive reward functions from observers, which is relevant for agents operating in environments where their objectives could be exploited if revealed.
This paper addresses the problem of privacy-preserving reinforcement learning, where the goal is to obscure the true reward function from an observer. The authors propose two dissimulation-based models, demonstrating that the resulting policies are deceptive and make it harder for human participants to infer the true reward function compared to an honest agent.
In this paper, we study the problem of deceptive reinforcement learning to preserve the privacy of a reward function. Reinforcement learning is the problem of finding a behaviour policy based on rewards received from exploratory behaviour. A key ingredient in reinforcement learning is a reward function, which determines how much reward (negative or positive) is given and when. However, in some situations, we may want to keep a reward function private; that is, to make it difficult for an observer to determine the reward function used. We define the problem of privacy-preserving reinforcement learning, and present two models for solving it. These models are based on dissimulation -- a form of deception that `hides the truth'. We evaluate our models both computationally and via human behavioural experiments. Results show that the resulting policies are indeed deceptive, and that participants can determine the true reward function less reliably than that of an honest agent.