Approximate Dynamic Programming for Stochastic
and Dynamic Decision Problems

The 4-day course deals with anticipatory methods for dynamic decision making. It will address the following questions:

1. What are the components of dynamic decision processes and how do they interact?
2. How can dynamic decision processes be modeled mathematically?
3. What methods exist in approximate dynamic programming?
4. How can they be applied to different types of problems?

In this course, we describe the process of approaching complex stochastic and dynamic decision problems with ADP-methods. We present the required steps from business problem over MDP to the ADP-solutions in detail and give an overview over the most prominent ADP-methods. We especially focus on offline learning methods known as value function approximation. The theoretical content of this course is accompanied by many illustrative examples from the field of logistics and by a serious gaming application.

Date:

25.-28.08.2025
 

Location:

Technische Universität Braunschweig,
Rebenring 58, Room RR58.4,
38106 Braunschweig

Language

English

Lecturers: