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Trends in Deep Reinforcement Learning with Kamyar Azizzadenesheli - #560

2022-02-21 · 78 min · episode 560 · 16 entities

Asserted relationships

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    Trends in Deep Reinforcement Learning with Kamyar Azizzadenesheli - #560
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    Feed author/publisher: Sam Charrington
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    professor at Purdue University
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    professor at Purdue University
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  • → hosted by TWIML company
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    Feed author/publisher: TWIML

Entities found in this episode

companys 7

  • mentioned TWIML company
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    evidence rules-v4
    Feed author/publisher: TWIML
  • mentioned Purdue University company
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    evidence rules-v4
    professor at Purdue University
  • mentioned Technology company
    0.50
    evidence rules-v4
    Feed category: Technology
  • works at Purdue University company
    0.50
    evidence rules-v4
    professor at Purdue University
  • works at Purdue University company
    0.50
    evidence rules-v4
    professor at Purdue University
  • discusses Technology company
    0.40
    evidence rules-v4
    Feed category: Technology
  • hosted by TWIML company
    0.40
    evidence rules-v4
    Feed author/publisher: TWIML

concepts 5

  • mentioned Science concept
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    Feed category: Science
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  • discusses Science concept
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    evidence rules-v4
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  • discusses Tech News concept
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    evidence rules-v4
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persons 3

  • mentioned Kamyar Azizzadenesheli person
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    Trends in Deep Reinforcement Learning with Kamyar Azizzadenesheli - #560
  • mentioned Sam Charrington person
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    evidence rules-v4
    Feed author/publisher: Sam Charrington
  • hosted by Sam Charrington person
    0.55
    evidence rules-v4
    Feed author/publisher: Sam Charrington

podcasts 1

Episode description as stored
Today we’re joined by Kamyar Azizzadenesheli, an assistant professor at Purdue University, to close out our AI Rewind 2021 series! In this conversation, we focused on all things deep reinforcement learning, starting with a general overview of the direction of the field, and though it might seem to be slowing, thats just a product of the light being shined constantly on the CV and NLP spaces. We dig into themes like the convergence of RL methodology with both robotics and control theory, as well as a few trends that Kamyar sees over the horizon, such as self-supervised learning approaches in RL. We also talk through Kamyar’s predictions for RL in 2022 and beyond. This was a fun conversation, and I encourage you to look through all the great resources that Kamyar shared on the show notes page at twimlai.com/go/560!