On December 1st 2022 the Reinforcement Learning Community had the pleasure to hold a workshop on applied reinforcement learning at @AWS in Vienna.
The three talks presented demonstrated versatility of applications deep reinforcement learning is able to tackle.
Our first speaker Sven Dominika presented current activities at Bosch Engineering to integrate reinforcement learning in automotive applications. His talk "Learning to drive (efficiently)" addressed the problem of optimal drive train control, where the goal is to balance the solution within the possibly contradicting objectives emission reduction, performance and efficiency.
The second talk was given by Behrooz Omidvar-Tehrani from AWS. In "Guided Text-based Item Exploration" he presented current activities at AWS to improve customer experience by helping users to make informed decisions.
In "Guided Text-based Item Exploration" he presented current activities at AWS to improve customer experience by integrating a text-based exploration model in their recommender engines.
Last but not least, our third speaker Kai Dresia presented literally rocket science. In his talk "Optimal Control of Rocket Engines Through Deep Reinforcement Learning" he sheds light on current activities at the Institute of Space Propulsion (DRL) and demonstrated the potential of deep reinforcement learning to optimally control liquid propellant rocket engines, especially in case of reusable applications.
Many thanks to our speakers, to our host Alexander Wrona (AWS) and also the organizing committee for making this workshop a huge success.