Instructor: Daniel Hennes
Secretary: Carola Stahl
Lectures: Tuesday, 17:30 – 19:00 (V38.03)
Tutorials: Wednesday, 14:00 – 15:30 (0.108) and Wednesday, 15:45 – 17:15 (0.447)
Office hours: by appointment
Communication: Announcements, such as change of schedule, will be communicated here and via the mailing list. Please subscribe to the mailing list to receive updates!
Slides and references to the additional material will be posted as the course proceeds. Last years material is available here. Most slides adapted from material by Richard S. Sutton and previous years.
- Wed. April 11: Admin
- Tue. April 17:
- Tue. April 24:
- Finite Markov Decision Processes
- room V38.01!
Tue. May 1:no lecture
- Tue. May 8: Dynamic Programming
- Tue. May 15: Monte Carlo Methods
Tue. May 22:no lecture
- Tue. May 29: Temporal-Difference Learning
Tue. June 5:no lecture
- Tue. June 12:
- Tue. June 19: Value-function Approximation
- Tue. June 26: Policy Gradients
- Tue. July 3: Imitation learning
Tue. July 10:no lecture
- Tue. July 17: Case studies
- Wed. July 18: Review + exam preparation (room will be announced later)
- Wed. July 25: Exam 11:00 – 13:00 in V38.01 (tbc!)
Tutorials take place on Wednesdays, from 14:00 – 15:30 in room 0.108 and from 15:45 – 17:15 in room 0.447. At the beginning of each tutorial, you should sign the attendance list and mark which exercises you have successfully completed. We will select students at random to present their solutions. You need to complete at least 50% of the exercises to be allowed to the final exam.
- Wed. April 25: Exercise 01 is due.
Wed. May 2
- Wed. May 9: Exercise 02 is due.
- Solution to Question 5
- Wed. May 16: Exercise 03 is due.
Wed. May 23
- Wed. May 30: Exercise 04 is due.
Wed. June 6:
- Wed. June 13: Exercise 05 is due.
- Wed. June 20: Exercise 06 is due.
Wed. June 27
- Wed. July 4: Exercise 07 will be due.
Wed. July 11 Wed. July 18(see above, review + exam prep)
- R. Sutton & A. Barto: Reinforcement Learning: An Introduction (2nd edition) (pdf)
- C. Szepesvari: Algorithms for Reinforcement Learning (pdf)
- S. M. LaValle: Planning Algorithms (pdf)
- J. Norris: Markov Chains