48 min
Reinforcement Learning (RL) - Andrew G. Barto | Podcast #25 The Engineered-Mind Podcast | Engineering, AI & Technology
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- Education
Andrew G. Barto is a former computer scientist and professor emeritus and known for his research on learning in machines and animals. He worked on developing learning algorithms that are useful for engineering applications but that also make contact with learning as studied by psychologists and neuroscientists.
When it comes to reinforcement learning (RL), it has been immensely gratifying for Andrew to participate in establishing new links between RL and methods from the theory of stochastic optimal control. Especially exciting are the connections between temporal difference (TD) algorithms and the brain's dopamine system.
Plenty of people know him as one of the authors of the great book “Reinforcement Learning: An Introduction” which he wrote with his colleague Richard Sutton.
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Andrew G. Barto is a former computer scientist and professor emeritus and known for his research on learning in machines and animals. He worked on developing learning algorithms that are useful for engineering applications but that also make contact with learning as studied by psychologists and neuroscientists.
When it comes to reinforcement learning (RL), it has been immensely gratifying for Andrew to participate in establishing new links between RL and methods from the theory of stochastic optimal control. Especially exciting are the connections between temporal difference (TD) algorithms and the brain's dopamine system.
Plenty of people know him as one of the authors of the great book “Reinforcement Learning: An Introduction” which he wrote with his colleague Richard Sutton.
—————————————————————————————
🧠 Free Science Community: community.sci-circle.com
👉 Science Academy: academy.jousefmurad.com
📥 Weekly free science insights newsletter: jousef.substack.com
🐤 Follow me on Twitter: @jousefm2
📷 Follow me on Instagram: @jousefmrd
Feel free to support the podcast on Patreon: patreon.com/theengiineer
48 min