General Information
Course Outline
The course is a basic introduction to reinforcement learning, including:
- Markov Decision Processes
- Optimal policy
- Finite Horizon
- Infinite discounted horizon
- Planning
- Value Iteration
- Policy Iteration
- Learning
- Model based
- Model free
- Q-learning
- TD-methods
- Actor-Critic
- Multi-Arm Bandits
- Partially Observable MDP
- Linear dynamics
The course will include both theory and applied reinforcement learning,
and a special emphasis will be put on algorithms.
Formalities
Location and Hours
Please check the course schedule.
Staff
li.ca.uat|ruosnam#ruosnaM yahsiY .forP (homepage)
- Teaching Assistant:
li.ca.uat.liam|2nehoceel#nehoC eeL
Feel free to coordinate reception hours with any of us via email.
Grade
Final Grade is made out of:
- 60% Exam
- 20% Homeworks
- 20% Project
As always, one has to pass the exam in order to pass the course.