  This paper proposes an observer-based framework for solving Partially
Observable Markov Decision Processes (POMDPs) when an accurate model is not
available. We first propose to use a Moving Horizon Estimation-Model Predictive
Control (MHE-MPC) scheme in order to provide a policy for the POMDP problem,
where the full state of the real process is not measured and necessarily known.
We propose to parameterize both MPC and MHE formulations, where certain
adjustable parameters are regarded for tuning the policy. In this paper, for
the sake of tackling the unmodeled and partially observable dynamics, we
leverage the Reinforcement Learning (RL) to tune the parameters of MPC and MHE
schemes jointly, with the closed-loop performance of the policy as a goal
rather than model fitting or the MHE performance. Illustrations show that the
proposed approach can effectively increase the performance of close-loop
control of systems formulated as POMDPs.
