Deep Reinforcement Learning Primer and Research Frontiers with Kamyar Azizzadenesheli

800 800 This Week in Machine Learning & AI

Today we’re joined by Kamyar Azizzadenesheli, PhD student at the University of California, Irvine, and visiting researcher at Caltech where he works with Anima Anandkumar, who you might remember from TWiML Talk 142.

We begin with a reinforcement learning primer of sorts, in which we review the core elements of RL, along with quite a few examples to help get you up to speed. We then discuss a pair of Kamyar’s RL-related papers: “Efficient Exploration through Bayesian Deep Q-Networks” and “Sample-Efficient Deep RL with Generative Adversarial Tree Search.” In addition to discussing Kamyar’s work, we also chat a bit of the general landscape of RL research today. So whether you’re new to the field want or to dive into cutting-edge reinforcement learning research with us, this podcast is here for you!

If you’d like to skip the Deep Reinforcement Learning primer portion of this and jump to the research discussion, skip ahead to the 34:30 mark of the episode.

Sign up for our Newsletter!

Be sure to sign up for our weekly newsletter. We recently shared a write up detailing the ML/AI Job Board we’re working on, and got a ton of encouragement and interest. To make sure you don’t miss anything, head over to twimlai.com/newsletter to sign up.

About Kamyar

Mentioned in the Interview

“More On That Later” by Lee Rosevere licensed under CC By 4.0

2 comments

Leave a Reply

Your email address will not be published.