  Reinforcement learning of real-world tasks is very data inefficient, and
extensive simulation-based modelling has become the dominant approach for
training systems. However, in human-robot interaction and many other real-world
settings, there is no appropriate one-model-for-all due to differences in
individual instances of the system (e.g. different people) or necessary
oversimplifications in the simulation models. This requires two approaches: 1.
either learning the individual system's dynamics approximately from data which
requires data-intensive training or 2. using a complete digital twin of the
instances, which may not be realisable in many cases. We introduce two
approaches: co-kriging adjustments (CKA) and ridge regression adjustment (RRA)
as novel ways to combine the advantages of both approaches. Our adjustment
methods are based on an auto-regressive AR1 co-kriging model that we integrate
with GP priors. This yield a data- and simulation-efficient way of using
simplistic simulation models (e.g., simple two-link model) and rapidly adapting
them to individual instances (e.g., biomechanics of individual people). Using
CKA and RRA, we obtain more accurate uncertainty quantification of the entire
system's dynamics than pure GP-based and AR1 methods. We demonstrate the
efficiency of co-kriging adjustment with an interpretable reinforcement
learning control example, learning to control a biomechanical human arm using
only a two-link arm simulation model (offline part) and CKA derived from a
small amount of interaction data (on-the-fly online). Our method unlocks an
efficient and uncertainty-aware way to implement reinforcement learning methods
in real world complex systems for which only imperfect simulation models exist.
