Deep Reinforcement Learning for Formation Control

Can Aykin, Martin Knopp, Klaus Diepold

Abstract: Continuing our work on using reinforcement learning for formation control, we present an end-to-end deep learning system which uses only camera images to learn to control the individual system's correct position within the formation. Mnih et al. created AIs playing video games utilizing the same visual input as a human player by employing convolutional neural networks for automatic feature extraction on images. This published work inspired us to employ a similar approach for processing the camera images and controlling the robot. We repeat the same experiment with two completely different camera positions. The results for both positions are very similar and such demonstrate the flexibility of the presented approach.