Reinforcement learning

Education facts

Code: 4MA903

Reinforcement learning (RL) is an interdisciplinary area of stochastic optimization, machine learning and optimal control. The underlying problem concerns how an intelligent agent ought to take actions in a dynamic environment in order to maximize the cumulative reward. In this course you will learn more about this problem, the corresponding mathematical framework and methods of RL, as well as its applications. Implementation in the form of computer programming is also an important element of the course.

Entry requirements

Courses in mathematics totalling at least 45 credits, including courses in linear algebra, probability theory and statistics, multivariable analysis, and at least 6 credits in programming or equivalent.

Education offerings

Each offering has its own dates and conditions. Closed offerings are retained as history and do not mean that a new application is open.

• Reinforcement learning

  Linnaeus University (Kalmar Växjö)

  Start date: 31 August 2026

  End date: 8 November 2026

  Pace of study: 50 %