Reinforcement learning is one of the most active research areas within machine learning. Recent empirical successes triggered a new wave of theoretical research in RL, with so many new directions opened in the past couple of years that it has become somewhat challenging to keep up with all the progress. This is made even worse in 2020 by the lack of in-person workshops and conferences.
This online seminar series aims to address these problems by providing a platform to get together and discuss the freshest work on RL theory. We aim to provide a balanced view of contemporary RL theory, and invite speakers covering a broad range of topics.
While the selection of the talks is naturally influenced by our personal tastes, we aim to keep these biases to a minimum. We welcome suggestions for talks at our email address below.
We are thankful to our co-hosts for guiding the discussions in the seminars:
Qinghua Liu
Akshay Krishnamurthy
Dylan Foster
Kwang-Sung Jun
Johannes Kirschner
David Janz
Nan Jiang
Ciara Pike-Burke
Gergely Neu
Csaba Szepesvári
Matteo Pirotta
Lin Yang
Daniel Russo
Tor Lattimore
Shipra Agrawal
Chi Jin
Vlad Tkachuk
Johannes Kirschner
We are grateful for the support of Universitat Pompeu Fabra, Imperial College London, University of Alberta and Google DeepMind. We would also like to thank Alex Ayoub for his help setting up and maintaining the meets.
Feel free to contact us with any queries or suggestions for papers at:
virtualrltheory[at]gmail.com