Seminar: Reinforcement Learning and Simulation-Based Search

SpeakerDavid Silver
AffiliationUCL
DateFriday, 08 Mar 2013
Time12:30 - 14:00
LocationCruciform B404 - LT2
Event seriesDeepMind CSML Seminar Series
Description

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).

iCalendar csml_id_79.ics