Reinforcement Learning Virtual Reading Group
We organise regular meetings to discuss recent papers from the reinforcement learning area. Meetings take place online and are open to everyone interested in reinforcement learning. Participants are expected to read the paper prior to the meeting.
To receive updates about meetings, sign up to the mailing list (the e-mails include links to calendar invites). You can vote for or propose papers for future meetings here. We also have a YouTube channel with recordings of past speakers.
Contact: Samuel Garcin and Kale-ab Tessera
Presenting your work at the Reading Group
We regularly host guest speakers, please get in touch using the contact above if you are interested in presenting your work. Below is a non-exhaustive list of past guest speakers.
- Eduardo Pignatelli, UCL
- Yifan Zhong, Peking University
- Joe Marino, Google DeepMind
- David Abel, Google DeepMind
- Matthias Gerstgrasser, Stanford & Harvard
- Geraud Tasse, University of Witwatersrand
- Emmanuel Bengio, Recursion
- Sasha Vezhnevets, Google Google DeepMind
- Bogdan Mazoure, Mila, Apple MLR
- Rihab Gorsane, InstaDeep
- Stephen McAleer, CMU
- Thomas Burns, Okinawa Institute of Science and Technology
- Charline Le Lan, Oxford
- Jason Ma, UPenn
- Jakob Bauer, Google DeepMind
- Minqi Jiang, UCL, Meta AI
- Jack Parker-Holder, Oxford
- Pablo Samuel Castro, Google Brain
- Rishabh Agarwal, Google Brain
- Mahdi Kazemi Moghaddam, University of Adelaide, Australian Institute for Machine Learning
- Mohamad H. Danesh, National University of Singapore
- Denis Yarats, NYU, Facebook AI Research
- Andrei Lupu, McGill University, MILA
- Alexander Sasha, Google DeepMind
- Jiahong Li, Beijing Union University
- Jacopo Castellini, University of Liverpool
- Robert Loftin, Microsoft Research
- Vitaly Kurin, Oxford
- Gregory Palmer, Leibniz University
- Jiachen Yang, Georgia Tech
- Ying Wen, UCL, Shanghai Jiao Tong University
- Greg Farquar, Oxford, Google DeepMind
- Maximilian Igl, Oxford, Waymo
Survey papers
We keep lists of survey papers about single-agent reinforcement learning and multi-agent reinforcement learning.