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Reinforcement Learning: Crash Course AI#9

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 Computer Science   |   Science   |   Technology
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Reinforcement learning is particularly useful in situations where we want to train AIs to have certain skills we dont fully understand ourselves. Unlike some of the techniques weve discussed so far, reinforcement learning generally only looks at how an AI performs a task AFTER it has completed it. And when an AI completes that task figuring out when and how to reward an AI, called credit assignment, is one of the hardest parts of reinforcement learning. So today, were going to explore these ideas, introduce a ton of new terms like value, policy, agent, environment, actions, and states and well show you how we can use strategies like exploration and exploitation to train John Green Bot to find things more efficiently next time.

Crash Course AI is produced in association with PBS Digital Studios:
https://www.youtube.com/user/pbsdigitalstudios/videos

Crash Course is on Patreon! You can support us directly by signing up at http://www.patreon.com/crashcourse

Thanks to the following patrons for their generous monthly contributions that help keep Crash Course free for everyone forever:

Eric Prestemon, Sam Buck, Mark Brouwer, Indika Siriwardena, Avi Yashchin, Timothy J Kwist, Brian Thomas Gossett, Haixiang N/A Liu, Jonathan Zbikowski, Siobhan Sabino, Zach Van Stanley, Jennifer Killen, Nathan Catchings, Brandon Westmoreland, dorsey, Kenneth F Penttinen, Trevin Beattie, Erika & Alexa Saur, Justin Zingsheim, Jessica Wode, Tom Trval, Jason Saslow, Nathan Taylor, Khaled El Shalakany, SR Foxley, Sam Ferguson, Yasenia Cruz, Eric Koslow, Caleb Weeks, Tim Curwick, David Noe, Shawn Arnold, William McGraw, Andrei Krishkevich, Rachel Bright, Jirat, Ian Dundore
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#CrashCourse #ArtificialIntelligence #MachineLearning

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