Workshop
Planning under uncertainty: Markov decision processes
- Christos Dimitrakakis (Chalmers University of Technology, Gothenburg, Sweden)
Abstract
This tutorial will give an introduction to decision theory and reinforcement learning. Starting from an introduction to preferences and utility, we will then cover sequential decision problems. These can be formalised as Markov decision processes. Within this framework lie many important problems such as adaptive experiment design and reinforcement learning. We will also discuss some foundational algorithms and models for reinforcement learning.
