While over many years we have witnessed numerous impressive demonstrations of the power of various reinforcement learning (RL) algorithms, and while much progress was made on the theoretical side as well, the theoretical understanding of the challenges that underlie RL is still rather limited. The best studied problem settings, such as learning and acting in finite state-action Markov decision processes, or simple linear control systems fail to capture the essential characteristics of seemingly more practically relevant problem classes, where the size of the state-action space is often astronomical, the planning horizon is huge, the dynamics is complex, interaction with the controlled system is not permitted, or learning has to happen based on heterogeneous offline data, etc. To tackle these diverse issues, more and more theoreticians with a wide range of backgrounds came to study RL and have proposed numerous new models along with exciting novel developments on both algorithm design and analysis. The workshop's goal is to highlight advances in theoretical RL and bring together researchers from different backgrounds to discuss RL theory from different perspectives: modeling, algorithm, analysis, etc.
This workshop will feature seven keynote speakers from computer science, operation research, control, and statistics to highlight recent progress, identify key challenges, and discuss future directions. Invited keynotes will be augmented by contributed talks, poster presentations, panel discussions, and virtual social events.
California Institute of Technology
Senior Research Scientist
CNRS Junior Researcher
We thank Hoang M. Le from providing the website template.