Deep learning for model-based RL
Friday, June 12, 2020
13:00 UTC
Prof Timothy Lillicrap
Google Deep Mind, University College London
Recording
Open recording
Hosted by The Neurotheory Forum70 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.
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