Experimental Platform for Deep Reinforcement Learning using 3D Simulation and a Physical Demonstrator of Rolling Mazes

A primary objective in the education of engineers is to develop students to be valuable employees for their future companies or competent founders of own businesses. Apart from positive impacts on individual life paths, this contributes to society as a whole. To meet the objective students must build up and strengthen skills and competencies required for their future profession. There is nothing wrong, though, with doing so by working on creative or even playful tasks 
that, strictly speaking, would not result in meaningful products. A focus on educational aspects is a chance to offer students challenging and highly motivating tasks, while our personal experience shows that high intrinsic motivation and creative working environments typically result in very good learning effects. 

The overall objective of our activities is to develop deep reinforcement agents, being a specific branch of artificial intelligence (AI), that learn to play the game in a software simulation as well as by controlling the original physical maze. In this paper we report on intermediate results on modelling and solving the game in a software environment and on the analysis of the physical game by image processing methods.