  In many settings, as for example wind farms, multiple machines are
instantiated to perform the same task, which is called a fleet. The recent
advances with respect to the Internet of Things allow control devices and/or
machines to connect through cloud-based architectures in order to share
information about their status and environment. Such an infrastructure allows
seamless data sharing between fleet members, which could greatly improve the
sample-efficiency of reinforcement learning techniques. However in practice,
these machines, while almost identical in design, have small discrepancies due
to production errors or degradation, preventing control algorithms to simply
aggregate and employ all fleet data. We propose a novel reinforcement learning
method that learns to transfer knowledge between similar fleet members and
creates member-specific dynamics models for control. Our algorithm uses
Gaussian processes to establish cross-member covariances. This is significantly
different from standard transfer learning methods, as the focus is not on
sharing information over tasks, but rather over system specifications. We
demonstrate our approach on two benchmarks and a realistic wind farm setting.
Our method significantly outperforms two baseline approaches, namely individual
learning and joint learning where all samples are aggregated, in terms of the
median and variance of the results.
