Speaker |
David Silver |
|---|---|
Affiliation |
UCL |
Date |
Friday, 08 March 2013 |
Time |
12:30-14:00 |
Location |
Zoom |
Link |
Cruciform B404 - LT2 |
Event series |
DeepMind/ELLIS CSML Seminar Series |
Abstract |
Simulation-based search is a highly successful paradigm for planning in challenging search spaces. Intuitively, the idea is to repeatedly imagine how the future might play out, and to learn from this imagined experience. Simulation-based search methods typically play out millions of sequences, and build up a large search tree of possible futures. By applying reinforcement learning (i.e. trial-and-error learning) to these sequences, it is possible to identify a near-optimal strategy in a computationally efficient manner. In this talk I will outline the relationship between reinforcement learning and simulation-based search, and show how reinforcement learning methods can be turned into powerful planning algorithms. Highlights of this approach include i) the world's first master-level computer Go program, ii) a program that convincingly defeated the built-in AI in Civilization II, and iii) the winning algorithm for the international POMDP planning competition (problems with hidden state). |
Biography |