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:
- What are the components of dynamic decision processes and how do they interact?
- How can dynamic decision processes be modeled mathematically?
- What methods exist in approximate dynamic programming?
- How can they be applied to different types of problems?
In this course, we describe the process to approach complex stochastic and dynamic decision problems with advanced 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 approximations. The theoretical content of this course is accompanied by many illustrative examples from the field of logistics and by a serious gaming application.
Technical University of Braunschweig