  In this paper we propose and compare methods for combining system
identification (SYSID) and reinforcement learning (RL) in the context of
data-driven model predictive control (MPC). Assuming a known model structure of
the controlled system, and considering a parametric MPC, the proposed approach
simultaneously: a) Learns the parameters of the MPC using RL in order to
optimize performance, and b) fits the observed model behaviour using SYSID. Six
methods that avoid conflicts between the two optimization objectives are
proposed and evaluated using a simple linear system. Based on the simulation
results, hierarchical, parallel projection, nullspace projection, and singular
value projection achieved the best performance.
