  Smart services are an important element of the smart cities and the Internet
of Things (IoT) ecosystems where the intelligence behind the services is
obtained and improved through the sensory data. Providing a large amount of
training data is not always feasible; therefore, we need to consider
alternative ways that incorporate unlabeled data as well. In recent years, Deep
reinforcement learning (DRL) has gained great success in several application
domains. It is an applicable method for IoT and smart city scenarios where
auto-generated data can be partially labeled by users' feedback for training
purposes. In this paper, we propose a semi-supervised deep reinforcement
learning model that fits smart city applications as it consumes both labeled
and unlabeled data to improve the performance and accuracy of the learning
agent. The model utilizes Variational Autoencoders (VAE) as the inference
engine for generalizing optimal policies. To the best of our knowledge, the
proposed model is the first investigation that extends deep reinforcement
learning to the semi-supervised paradigm. As a case study of smart city
applications, we focus on smart buildings and apply the proposed model to the
problem of indoor localization based on BLE signal strength. Indoor
localization is the main component of smart city services since people spend
significant time in indoor environments. Our model learns the best action
policies that lead to a close estimation of the target locations with an
improvement of 23% in terms of distance to the target and at least 67% more
received rewards compared to the supervised DRL model.
