  In deep reinforcement learning, building policies of high-quality is
challenging when the feature space of states is small and the training data is
limited. Despite the success of previous transfer learning approaches in deep
reinforcement learning, directly transferring data or models from an agent to
another agent is often not allowed due to the privacy of data and/or models in
many privacy-aware applications. In this paper, we propose a novel deep
reinforcement learning framework to federatively build models of high-quality
for agents with consideration of their privacies, namely Federated deep
Reinforcement Learning (FedRL). To protect the privacy of data and models, we
exploit Gausian differentials on the information shared with each other when
updating their local models. In the experiment, we evaluate our FedRL framework
in two diverse domains, Grid-world and Text2Action domains, by comparing to
various baselines.
