OverviewThis course gives a thorough overview of the fundamental theory of reinforcement learning, as well as a look into advanced algorithms and modern proof techniques for reinforcement learning problems. Each module lasts for about a week, and includes a short lecture of basic concepts, an overview of proofs and some handson work. Assessment is through assignments (including reviewing and implementation of research papers) and a mini project. ModulesThis is only a suggested schedule. Points in italics are optionally covered.
 Course organisation and materialThe course takes place in LP3/4 and is worth 7.5 credits. ScheduleStart date: 13 February. Go here, or email me at chrdimi at chalmers.se if you want to join. Tuesdays and Thursdays, 13:3015:00. [Calendar] Locations: EDIT 8103.
ReadingsThe course mainly follows the structure of our draft book, "Decision Making Under Uncertainty and Reinforcement Learning". Other material will be referred to in the reading assignments. Assignments and miniprojectsThere will be 3 assignments focusing on the fundamentals. Reading assignments on Thursdays include a discussion leader, who is also responsible for presenting Tuesday's introductory material. A larger miniproject combining elements of the assignments for optimal exploration, will take place at the end of the course. Preparatory meetingsThese informal meetings are there to cover some basic ground, and help the teaching staff select material. Students do not need to attend.
