Reinforcement Learning Bootcamp

September 25-27, 2024
PLUS University Salzburg

Attention: New Location! Please check the Directions page for more information.

What is this bootcamp about?

Reinforcement learning (RL) is a powerful learning paradigm of machine learning (ML). Reinforcement Learning is at the cutting edge of artificial intelligence, powering advancements in robotics, autonomous systems, game playing, and decision-making algorithms. Mastering RL opens up a world of opportunities in both research and industry, enabling you to contribute to groundbreaking innovations.

Capturing your control problem as a meaningful Markov Decision Process (MDP) is not trivial. Additional challenges arise in the training in terms of stability and evaluation. Other practical aspects include reproducibility, efficiency, implementation, deployment in hardware, and choosing the most suitable algorithm for your problem.

RL applications are very promising, but have been deployed in real machines only a handful of times. This workshop aims at lowering the barrier in applying RL and making it a more used tool.

Who should attend?

• Target Audience: Ideal for students, professionals, and enthusiasts with a basic understanding of machine learning who want to deepen their knowledge in reinforcement learning.
• Expert Instruction: Learn theory and hands-on techniques from leading experts with real-world experience in applying RL to solve complex problems.
• Collaborative Learning Environment: Engage with peers who share your passion for AI and RL in a collaborative atmosphere.
• Advanced Content: Stay ahead of the curve with lessons on the latest RL algorithms, tools, and best practices.
• Inspiring Location: The stunning setting of Salzburg is meant to inspire your learning journey and fuel your creativity.

Please register here for free

Keynotes

The event is hosted by the Smart Analytics and Reinforcement Learning (SARL) team at IDA Lab, PLUS University of Salzburg, in collaboration with Fraunhofer Austria and AI Austria. It is offered free of charge to ensure accessibility for all those interested in Reinforcement Learning. We are thrilled about our lineup of keynote speakers and eagerly anticipate their presentations!

Razvan Pascanu Research Scientist Google DeepMind

Razvan Pascanu is a research scientist at Google Deepmind. He received his PhD from University of Montreal under the supervision of Prof. Yoshua Bengio. His research focuses on several key areas: understanding how to optimize deep models efficiently and at scale, making learning more data-efficient, particularly in Reinforcement Learning (RL) and exploring the dynamics of learning, especially under gradient descent conditions.

Kai Dresia Research Scientist German Aerospace Center (DLR)

Kai Dresia received his master's degree in aerospace engineering from RWTH Aachen University, Germany. He is a research scientist for intelligent rocket engine control systems at the German Aerospace Center (DLR). The goal of his research is to develop and experimentally test an intelligent rocket engine controller for future liquid propellant rocket engines.

Anton Fuxjaeger EnliteAI Gmbh Head of Reinforcement Learning

Anton Fuxjaeger received his master's degree in Pervasive Parallelism (AI) from University of Edinburgh, Scotland. He leads EnliteAI's research and development team in the area of Reinforcement Learning. The goal of his research is applied Reinforcement Learning, with a strong focus on power grid optimization. He is also developing the open source RL framework Maze (enlite-ai/maze).

Michael Somma AI Researcher in Cybersecurity Joanneum Research

As an AI Researcher at Joanneum Research in Graz, he brings a broad portfolio in artificial intelligence in academic and commercial settings. As a Research Associate at the University of Vienna, he gained experience applying various machine learning techniques, including computer vision and regression models, contributing to two FWF-funded research projects. At Deloitte, he led the deployment of advanced natural language processing solutions and was instrumental in pioneering RL applications. His expertise spans developing automated trading systems with RL and integrating RL into physical simulations.

Organizers

Simon Hirlaender
SARL Team lead, University of Salzburg Sabrina Pochaba
SARL Team, University of Salzburg Olga Mironova
SARL Team, University of Salzburg Catherine Laflamme
Fraunhofer Austria Andreas Windisch
AI Austria Thomas Gallien
AI Austria Marius-Constantin Dinu
AI Austria