  The growing number of applications of Reinforcement Learning (RL) in
real-world domains has led to the development of privacy-preserving techniques
due to the inherently sensitive nature of data. Most existing works focus on
differential privacy, in which information is revealed in the clear to an agent
whose learned model should be robust against information leakage to malicious
third parties. Motivated by use cases in which only encrypted data might be
shared, such as information from sensitive sites, in this work we consider
scenarios in which the inputs themselves are sensitive and cannot be revealed.
We develop a simple extension to the MDP framework which provides for the
encryption of states. We present a preliminary, experimental study of how a DQN
agent trained on encrypted states performs in environments with discrete and
continuous state spaces. Our results highlight that the agent is still capable
of learning in small state spaces even in presence of non-deterministic
encryption, but performance collapses in more complex environments.
