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.
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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.
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Mentioned in the Interview
- Paper: Efficient Exploration through Bayesian Deep Q-Networks
- Paper: Sample-Efficient Deep RL with Generative Adversarial Tree Search
- Epsilon Greedy
- Paper: A Tutorial on Thompson Sampling
- Tensor Operations for Machine Learning with Anima Anandkumar – Talk 142
- TWIML Presents: Series page
- TWIML Events Page
- TWIML Meetup
- TWIML Newsletter
“More On That Later” by Lee Rosevere licensed under CC By 4.0