This course is entirely based on the instrumental course of Sergey Levine taught at Berkeley CS189. Hence full credit should be given to Sergey Levine and his team for creating such great materials. This course follows its structure and homeworks except for the final project. It is one of the optional courses of the IASD M2 program

This course will assume some familiarity with reinforcement learning, numerical optimization, and machine learning. For introductory material on RL and MDPs, see the Berkeley CS188 EdX course, starting with Markov Decision Processes I, as well as Chapters 3 and 4 of Sutton & Barto. It is worth also checking the introductory course on machine learning Berkeley CS189.

There will be weekly labs whose result needs to be posted to .Moodle. In addition, there will a course project broken into two parts:

• Mid term project
• final project

We will post slides on the front page after each lecture.

All homeworks should be done individually.

For the project, you may work in groups of up to three people. Each group will make a live presentation

All assignments must be turned in via Gradescope on time. We will allow a total of five late days cumulatively. We will not make any additional allowances for late assignments: the late days are intended to provide for exceptional circumstances, and students should avoid using them unless absolutely necessary. Any assignments that are submitted late (with insufficient late days remaining) will not be graded.

Late days may not be used for quizzes, final project proposals, final project milestone reports, final project reports, or any of the project peer review reports, only for the five homeworks.

• Homework: 50% (10% per HW x 5 HWs)
• Final Project: 50%