Stefan Stojanovic

I am a 4th year PhD student at the Division of Decision and Control Systems (DCS) of the School of Electrical Engineering and Computer Science. I carry my research under the supervision of Prof. Alexandre Proutiere. Prior to that, I completed a MSc degree in Electrical Engineering and Information Technology from ETH Zurich with distinction.

Research

My primary research area is reinforcement learning (RL). Currently, I am interested in self-supervised RL, which involves learning to act without specific reward signals. For example, we recently introduced and studied the framework of switching successor measures, which enables hierarchical zero-shot reinforcement learning. On the theoretical side, we have recently investigated conditions under which structures essential for solving a task naturally emerge in self-supervised RL. Previously my focus has been on establishing theoretical guarantees for problems with a specific low-dimensional structure in settings such as model-based RL, contextual bandits, and model-free RL.

If you are interested in my publications (Neurips, ICML, ALT...) please check my Google Scholar page. My research is supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP-AI), funded by the Knut and Alice Wallenberg Foundation.

Teaching & Supervision

I have been a teaching assistant for the Reinforcement Learning course during 2023-2025. In addition to the regular teaching activities, I designed and developed a new set of exercise sessions and educational materials for the course, available here.

I have also supervised bachelor's and master's theses related to my research area. Here are some examples of the work I've supervised:

• "Goal-Conditioned Reinforcement Learning for Autonomous Perceptive Earthworks in Particle-Based Soil Simulation" by Andrea Cucchietti (co-supervised with ETH Robotic Systems Lab)

• "Energy-Aware Adaptive Video Streaming Using Deep Reinforcement Learning" by Baptiste Boutaud de la Combe (co-supervised with InterDigital)

• “Adaptive Reinforcement Learning for Real-World Systems with Delays” by Iga Pawlak (co-supervised with ABB)

• “Frameskipping and Exploration Strategies for Deep Q-Networks” by Niklas Rolin and Vaka Soleyjardottir

• “Multi-Agent Control in Warehousing: A Deep Q-Network Approach” by Adam Fischer and Martin Wilen

If you are a bachelor's or master's student at KTH interested in working on Reinforcement Learning, please contact me via email.