The purpose of this course is to allow students to acquire a solid understanding of the theoretical foundations of reinforcement learning. The topics will range from building up foundations (Markovian Decision Processes and the various special cases of it), to discussing solutions to the three core problem settings: Online reinforcement learning, batch reinforcement learning and planning/simulation optimization. In each of these settings, we cover key algorithmic challenges and the core ideas to address these.
- Instructor: Csaba Szepesvari