  In this paper, we propose a Model-Based Reinforcement Learning (MBRL)
algorithm for Partially Measurable Systems (PMS), i.e., systems where the state
can not be directly measured, but must be estimated through proper state
observers. The proposed algorithm, named Monte Carlo Probabilistic Inference
for Learning COntrol for Partially Measurable Systems (MC-PILCO4PMS), relies on
Gaussian Processes (GPs) to model the system dynamics, and on a Monte Carlo
approach to update the policy parameters. W.r.t. previous GP-based MBRL
algorithms, MC-PILCO4PMS models explicitly the presence of state observers
during policy optimization, allowing to deal PMS. The effectiveness of the
proposed algorithm has been tested both in simulation and in two real systems.
