Tuesday, July 07, 2026, 9:00 - Friday, July 10, 2026, 17:00

ECC 2026

Model Predictive Control (MPC) and Reinforcement Learning (RL) represent two prominent frameworks for optimal decision-making under uncertainty. Both fields stem from similar fundamental principles, and are widely used in practical applications, including robotics, process control, energy systems, and autonomous driving. Despite their similarities, MPC and RL follow distinct paradigms that emerged from different communities and requirements. While interest in combining these fields has grown significantly, the resulting landscape of hybrid methods can be difficult to navigate. This tutorial session aims to provide both control researchers and practitioners with a comprehensive overview on the theoretical foundations, algorithmic architectures, and open challenges essential for the synthesis of MPC and RL. Furthermore, it focuses on state-of-the-art software aimed at rapid development of novel methods combining the two paradigms.
 

... more information coming soon!