Two papers accepted for our second participation at LOD 2023 conference

Lake Windermere on a misty morning (By Mkonikkara, CC BY-SA 3.0, via Wikimedia Commons)

For the second time we (Raphael Engelhardt and Wolfgang Konen) have been given the opportunity to present our work at the Conference on machine Learning, Optimization and Data science (LOD) conference.

To this year's 9^th edition, held in Grasmere, England, UK on September 22^nd - 26^th we have the honor to contribute even two papers stemming from the fruitful collaboration with our Dataninja-colleagues at Ruhr-University Bochum, Prof. Laurenz Wiskott and PhD student Moritz Lange:

1. Our work entitled “Ökolopoly: Case Study on Large Action Spaces in Reinforcement Learning” describes how we translate the cybernetic board game Ökolopoly into the realm of reinforcement learning and evaluate various methods of handling large observation and action spaces. Large spaces pose a serious challenge to reinforcement learning and we hope our case study will provide valuable approaches to fellow researchers. Additionally we make the environment available to the scientific community with Open AI Gym compatible API.
2. “Improving Reinforcement Learning Efficiency with Auxiliary Tasks in Non-Visual Environments: A Comparison”, under the first authorship of Moritz Lange, is a thorough comparison of auxiliary tasks in a variety of control and robotic tasks, and shows how agents benefit from decoupled representation learning of auxiliary tasks in complex environments.

We are very grateful for this opportunity, look forward to hear other researchers’ advances in machine learning, and interesting discussions about current research topics.