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\nReinforcement Learning\n

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\n\nPublisher:\n\nCrash Course\n\n\n
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\nReinforcement learning is particularly useful in situations where we want to train AIs to have certain skills we don’t fully understand ourselves. Unlike some of the techniques we’ve discussed so far, reinforcement learning generally...\nReinforcement learning is particularly useful in situations where we want to train AIs to have certain skills we don’t fully understand ourselves. Unlike some of the techniques we’ve 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, we’re going to explore these ideas, introduce a ton of new terms like value, policy, agent, environment, actions, and states and we’ll show you how we can use strategies like exploration and exploitation to train John Green Bot to find things more efficiently next time. \n
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Resource Details

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Curator Rating
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Educator Rating
Not yet Rated
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Media Length
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11:06
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Grade
12th - Higher Ed
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Subjects
STEM
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Media Type
Instructional Videos
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Source:
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\nCrash Course Artificial Intelligence\n\n
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Date
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2019
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Language
English
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Audiences
For Teacher Use
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Usage Permissions
Fine Print: Educational Use
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Concepts

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john green, cells, values, artificial intelligence, computer science, exploitation
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Additional Tags

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environment, learning, robot, path, game states, crash course, drive home, higher, agent, powerful tool, move, reinforcement learning, moving, credit assignment, value, battery, game, understand, tall shelf, policy, walk, machine learning, states, actions, ai, reward, action, exploration, cookie, black hole, learn, game board, 100 actions, reinforcement, find
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Classroom Considerations

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Best For: Explaining a topic
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