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Deep Reinforcement Learning Series – Part 3: function approximators

May 13, 2017 @ 10:00 am - 11:00 am

Metis is hosting Chi-Town Machine Learning for their Deep Reinforcement Learning Series – Part 3: Function Approximators with Jeremy Watt.

Breakfast will be provided!

In this series of tutorial talks we will be Deep Reinforcement Learning from start to finish – the tech powering self-playing Atari games, Alpha Go, problems in automatic control and more.  

In Part 3 of the series we will be covering extensions of Q-Learning to problems – like chess, control, and video games – where the enormous size of the state space makes resolving Q – directly – impossible.  As cycling through even a reasonable portion of the states is computationally impossible, function approximators are introduced to greatly generalize the effect of a Q-Learner.

This talk will be highly interactive with a number of live code demonstrations and a fully featured Jupyter Notebook. 

If you did not attend previous events in this series be sure to check out the resources listed in those events – including slides, blog posts, etc.,


May 13, 2017
10:00 am - 11:00 am
Event Category:


Metis Chicago
1033 West Van Buren St, 3rd Floor
Chicago, IL us


Metis: Chicago Data Science