# CS 185/285 Syllabus https://rail.eecs.berkeley.edu/deeprlcourse/syllabus#late # [CS 185/285](https://rail.eecs.berkeley.edu/deeprlcourse/) * [Calendar](https://rail.eecs.berkeley.edu/deeprlcourse/calendar) * [Resources](https://rail.eecs.berkeley.edu/deeprlcourse/resources) * [Syllabus](https://rail.eecs.berkeley.edu/deeprlcourse/syllabus) * [Staff](https://rail.eecs.berkeley.edu/deeprlcourse/staff) * Menu * [Calendar](https://rail.eecs.berkeley.edu/deeprlcourse/calendar) * [Resources](https://rail.eecs.berkeley.edu/deeprlcourse/resources) * Menu * [Resources](https://rail.eecs.berkeley.edu/deeprlcourse/resources) * Menu ### [Calendar](https://rail.eecs.berkeley.edu/deeprlcourse/calendar) ### [Resources](https://rail.eecs.berkeley.edu/deeprlcourse/resources) * [Previous Offerings](https://rail.eecs.berkeley.edu/deeprlcourse/resources#prevoffs) * [Courses](https://rail.eecs.berkeley.edu/deeprlcourse/resources#courses) * [Relevant Textbooks](https://rail.eecs.berkeley.edu/deeprlcourse/resources#textbooks) * [Misc Links](https://rail.eecs.berkeley.edu/deeprlcourse/resources#misclinks) ### [Syllabus](https://rail.eecs.berkeley.edu/deeprlcourse/syllabus) * [Prerequisites](https://rail.eecs.berkeley.edu/deeprlcourse/syllabus#prereqs) * [Technology](https://rail.eecs.berkeley.edu/deeprlcourse/syllabus#technology) * [Materials](https://rail.eecs.berkeley.edu/deeprlcourse/syllabus#materials) * [Collaboration](https://rail.eecs.berkeley.edu/deeprlcourse/syllabus#collaboration) * [Late Policy](https://rail.eecs.berkeley.edu/deeprlcourse/syllabus#late) * [Grading](https://rail.eecs.berkeley.edu/deeprlcourse/syllabus#grading) ### Meta * [Staff](https://rail.eecs.berkeley.edu/deeprlcourse/staff) CS 185/285 at UC Berkeley # Syllabus ## [Prerequisites](https://rail.eecs.berkeley.edu/deeprlcourse/syllabus/#prereqs) [CS189](http://www.eecs189.org/) or equivalent is a prerequisite for the course. This course will assume some familiarity with reinforcement learning, numerical optimization, and machine learning. For introductory material on RL and MDPs, see the [CS188 EdX course](http://ai.berkeley.edu/), starting with _Markov Decision Processes I_ , as well as Chapters 3 and 4 of [Sutton & Barto](http://webdocs.cs.ualberta.ca/~sutton/book/the-book.html). ## [Technology](https://rail.eecs.berkeley.edu/deeprlcourse/syllabus/#technology) [Ed](https://edstem.org) will be used for announcements, general questions and discussions, clarifications about assignments, student questions to each other, and so on. If you are a UC Berkeley student enrolled in the course, and haven't already been added to Ed, please [email the staff](https://rail.eecs.berkeley.edu/deeprlcourse/staff). [Gradescope](https://www.gradescope.com) will be used to collect and grade assignments. If you are a UC Berkeley student enrolled in the course, and haven't already been added to Gradescope, please [email the staff](https://rail.eecs.berkeley.edu/deeprlcourse/staff). If you are not a UC Berkeley student or not enrolled, but are interested in following and discussing the course, there is a subreddit forum here that we will try to monitor: [reddit.com/r/berkeleydeeprlcourse/](https://www.reddit.com/r/berkeleydeeprlcourse/) ## [Materials](https://rail.eecs.berkeley.edu/deeprlcourse/syllabus/#materials) All materials can be found on [the front page](https://rail.eecs.berkeley.edu/deeprlcourse/). ### Homeworks There will be five homeworks. For each homework, we will post a PDF on [the front page](https://rail.eecs.berkeley.edu/deeprlcourse/) and starter code on [Github](https://github.com/berkeleydeeprlcourse/homework_spring2026). Please see the front page homeworks section for assignment deadlines. ### Slides We will post slides [on the front page](https://rail.eecs.berkeley.edu/deeprlcourse/) 24 hours before each lecture. ### Videos Lectures will be recorded, and recordings are available on bCourses. Please check that you can access bCourses and see the Media Gallery. ## [Course Logistics](https://rail.eecs.berkeley.edu/deeprlcourse/syllabus/#logistics) ### Sections Sections are led by the TAs starting in Week 2. Schedule: Tue 1p-2p (Cory 521) Tue 6p-7p (Soda 306) Wed 3p-4p (Social Sciences Building 60) ### Midterm Exam We will hold a midterm exam late in the semester, during the week of April 15. The exact date and time will be posted soon. ### Mini-Quizzes Starting with Lecture 2, each lecture has a short Gradescope mini-quiz due within 7 days of the lecture (for example, the Jan 23 lecture quiz is due Jan 30). Quizzes are quick, and you can retake them by completing the "second try" quiz if you want to improve your score. ## [Collaboration](https://rail.eecs.berkeley.edu/deeprlcourse/syllabus/#collaboration) All homeworks should be done individually. For the final project, you may work in groups of up to three people. Each group will submit a report. The expectations for the project scope will increase depending on the number of students in each group, and for groups of two or three, we will also expect a short paragraph to explain the role of each group member along with the final report. From past experience, groups of two tend to be the most effective, though you may work in a group of three or alone. Groups larger than three are not permitted without special permission from the course staff. Note that you should form your groups before TBD, though you are strongly encouraged to do this much sooner so that you can start on your project. ## [Late Policy](https://rail.eecs.berkeley.edu/deeprlcourse/syllabus/#late) 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 outlines, final project milestone reports, final project reports, or any of the project peer review reports, only for the five homeworks. ## [Grading](https://rail.eecs.berkeley.edu/deeprlcourse/syllabus/#grading) The grade in the course will be determined by programming homeworks (50%, 10% each), final project (20%), exam (20%), and mini-quiz (10%). ### [Calendar](https://rail.eecs.berkeley.edu/deeprlcourse/calendar) ### [Resources](https://rail.eecs.berkeley.edu/deeprlcourse/resources) * [Previous Offerings](https://rail.eecs.berkeley.edu/deeprlcourse/resources#prevoffs) * [Courses](https://rail.eecs.berkeley.edu/deeprlcourse/resources#courses) * [Relevant Textbooks](https://rail.eecs.berkeley.edu/deeprlcourse/resources#textbooks) * [Misc Links](https://rail.eecs.berkeley.edu/deeprlcourse/resources#misclinks) ### [Syllabus](https://rail.eecs.berkeley.edu/deeprlcourse/syllabus) * [Prerequisites](https://rail.eecs.berkeley.edu/deeprlcourse/syllabus#prereqs) * [Technology](https://rail.eecs.berkeley.edu/deeprlcourse/syllabus#technology) * [Materials](https://rail.eecs.berkeley.edu/deeprlcourse/syllabus#materials) * [Collaboration](https://rail.eecs.berkeley.edu/deeprlcourse/syllabus#collaboration) * [Late Policy](https://rail.eecs.berkeley.edu/deeprlcourse/syllabus#late) * [Grading](https://rail.eecs.berkeley.edu/deeprlcourse/syllabus#grading) ### Meta * [Staff](https://rail.eecs.berkeley.edu/deeprlcourse/staff)