- Title: Challenges of deep reinforcement learning, and the example of AlphaStar
- Speaker: Tom Schaul (Google DeepMind)
- Time and date: 5pm to 6pm, March 12th, 2020 (Wednesday)
- Room: Peston Lecture Theatre, Mile End campus
The talk will be followed by informal drinks and nibbles at The EECS Hub at 6pm.
All welcome (especially students), no pre-booking required
Tom Schaul has been a researcher at DeepMind for 6 years. His research is focused on reinforcement learning with deep neural networks, but includes modular and continual learning, black-box optimization, temporal and state abstractions, off-policy learning about many goals simultaneously, and video-game benchmarks. Tom grew up in Luxembourg and studied computer science in Switzerland (with exchanges at Waterloo and Columbia), where he obtained an MSc from the EPFL in 2005. He holds a PhD from TU Munich (2011), which he did under the supervision of Jürgen Schmidhuber at the Swiss AI Lab IDSIA. From 2011 to 2013, he did a postdoc with Yann LeCun at the Courant Institute of NYU.