This event has ended. View the official site or create your own event → Check it out
This event has ended. Create your own
View analytic
Monday, December 7 • 15:30 - 17:30
Introduction to Reinforcement Learning with Function Approximation

Sign up or log in to save this to your schedule and see who's attending!

Reinforcement learning is a body of theory and techniques for optimal sequential decision making developed in the last thirty years primarily within the machine learning and operations research communities, and which has separately become important in psychology and neuroscience. This tutorial will develop an intuitive understanding of the underlying formal problem (Markov decision processes) and its core solution methods, including dynamic programming, Monte Carlo methods, and temporal-difference learning. It will focus on how these methods have been combined with parametric function approximation, including deep learning, to find good approximate solutions to problems that are otherwise too large to be addressed at all. Finally, it will briefly survey some recent developments in function approximation, eligibility traces, and off-policy learning.

Monday December 7, 2015 15:30 - 17:30
Level 2 room 210 AB

Attendees (21)