Lectures
Lectures will be places of discussion where the current topic is summarized by the lecturer and discussed among all present. The students are expected to prepare by reading given material in advance prior to each lecture.
- FileCourse arrangements FilePDF document
- FileLecture 1: Overview FilePDF document
- FileLecture 2: Markov decision processes FilePDF document
- FileLecture 3: Reinforcement learning FilePDF document
This lecture recording is from the 2021 instance of the course.
- FileLecture 4: Function approximation FilePDF document
- FileLecture 5: Policy gradient FilePDF document
- FileLecture 6: Actor-critic methods FilePDF document
- FileLecture 9: Exploration and exploitation FilePDF document
- FileLecture 11: Partially observable MDPs FilePDF document