Deep learning for model-based RL
Schedule
Friday, June 12, 2020
13:00 UTC
Timothy Lillicrap
Prof
Google Deep Mind, University College London
Host: The Neurotheory Forum
Recording
Open recording
Event Information
Recording
Available
Host
The Neurotheory Forum
Duration
70 minutes
Abstract
Model-based approaches to control and decision making have long held the promise of being more powerful and data efficient than model-free counterparts. However, success with model-based methods has been limited to those cases where a perfect model can be queried. The game of Go was mastered by AlphaGo using a combination of neural networks and the MCTS planning algorithm. But planning required a perfect representation of the game rules. I will describe new algorithms that instead leverage deep neural networks to learn models of the environment which are then used to plan, and update policy and value functions. These new algorithms offer hints about how brains might approach planning and acting in complex environments.
Topics
Related Job Opportunities
JobJobJob
PhD Studentship: Mitochondrial Metabolism and Novel Therapeutic Strategies for Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) (Fixed Term)
Supervisors: Professor Andrew Murray, Department of Physiology, Development and Neuroscience, University of Cambridge Dr Ross Lindsay, Novo Nordisk Funding: Fully funded PhD studentship (Home/UK…
Posted Aug 10, 2026
Research Associate (Fixed Term)
We seek a highly motivated Postdoctoral Research Associate to join the laboratory of Professor Kathy Niakan. We are based in the Loke Centre for Trophoblast Research (LCTR), in the Department of…
Posted Aug 11, 2026
Research Assistant/Associate (Fixed Term)
Applications are invited for a postdoctoral research associate position to study the neural mechanisms of visual learning in mice, in the laboratories of Professor Ole Paulsen and Dr Jasper Poort…
Posted Aug 11, 2026