While in many applications learning has become the prevailing methodology, process control is still a field where control engineering cannot be replaced for many low level control problems, mainly due to lack of stability of learned controllers, and computational complexity in embedded settings. DeLiCio proposes fundamental research on the crossroads of ML/IA and CT with planned algorithmic contributions on the integration of models, prior knowledge and learning in control and the perception action cycle: data-driven learning and identification of physical models for control; state representation learning for control; stability and robustness priors for reinforcement learning; stable decentralized (multi-agent) control using ML and CT. The planned methodological advances of this project will be evaluated on a challenging application requiring planning as well as fine-grained control, namely the synchronization of a UAV swarm through learning. The objective is to learn strategies, which allow a swarm to solve a high-level goal (navigation, visual search) while at the same time maintaining a formation.