Reinforcement Learning

About this programme

The aim of the course is to introduce basic as well as modern concepts of reinforcement learning. This includes Markov decision processes, dynamic programming, model-free prediction and control, temporal difference learning, function approximation methods, policy gradient and actor-critic methods, and modern applications of reinforcement learning. The course consists of two modules: theory (3.5 credits) and project (4 credits). Instruction is given in the form of lectures, exercise sessions and supervision. Both parts of the course are graded on a scale from A to F, where A to E are passing grades. To complete the course, a passing grade is required on both parts, and the final grade of the course is determined by weighing the grades from the course modules, where each grade is weighed in relation to the scope of the course module. The course literature on this course is available for free via the university library and arxiv.org respectively. See the list of course literature or the course page for links. To follow your passion. To be close to international career opportunities. To explore city and countryside. In Stockholm you can really get the best of both worlds. Here we explain all the different steps you need to go through when applying to a course or programme. Stockholm University arranges regular webinars and participates in educational fairs and events virtually and around the world to meet students and inform them about our study programmes. Every year Stockholm University welcomes thousands of international students from all over the world. Each bringin