- Title: Agents with internal models
- Speaker: Theophane Weber
- Time and date: 4pm to 5pm, Feb 6, 2019
- Room: BR 3.02, Bancroft Road Teaching Rooms, QMUL Mile End Campus
On Wednesday 6th February 2019 the Game AI Group will host a seminar by Theophane Weber from DeepMind. Followed by drinks in the Informatics Hub. All welcome (especially students), no pre-booking required.
I will present recent work that studies agents endowed with an internal model of the world. This will include agents that learn world models by predicting the future, and learn to interpret those predictions in order to act better without suffering from model inaccuracies; agents with neural analogues of search algorithms such as Monte Carlo Tree Search; agents that learn temporally abstract models of the world in order to compute representations of their belief about the state of the world, agents that use their models to evaluate counterfactual scenarios and learn from those synthetic experiences, and agents with only implicit models of the world that still exhibit planning-like behavior.
I am a staff research scientist at DeepMind. My research interests span deep reinforcement learning, model-based RL and planning, probabilistic modeling and modeling of uncertainty. Prior to DeepMind, I worked at Lyric Labs, a skunkworks team of Analog Devices, working on applications of machine learning to the physical world. I hold an M.S. and Ph.D from MIT in Operations Research and an M.S. from Ecole Centrale Paris in Applied Mathematics.