Understanding the theory and the most important algorithms of reinforcement learning as well as the ability to implement them in order to solve realistic problems.

Reinforcement learning is a field of artificial intelligence and is concerned with the development of strategies that an agent uses to maximize its reward in a random environment in a model-free manner.

Applications include robotics (OpenAI gym), computer vision, games (such as Go, chess, Atari 2600, or Dota 2) at the human level or above and many more.

Theory and algorithms of reinforcement learning:

• Introduction
• Bandit problems
• Markov decision problems
• Bellman equations
• Hamilton-Jacobi-Bellman equation
• Dynamic programming
• Monte-Carlo learning
• Temporal-difference learning
• Tabular methods
• Function approximation and deep learning
• On-policy vs. off-policy
• Eligibility traces
• Policy gradients and actor-critic
• Applications

In the tutorial, the theory will be repeated and extended and the algorithms will be implemented.

Two tests, tutorial, and participation.

 Lecture notes (in English) will be handed out.

The theoretical aspects will be explained in the lectures in a self-contained manner so that the course can be taken during or after the fourth semester.