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Reinforcement Learning: Crash Course AI#9

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Reinforcement 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.

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34 thoughts on “Reinforcement Learning: Crash Course AI#9”

  9. I don't agree with the bagel/donut choice example. Why choose the option of two bagels or donuts vs. the greater risk of more donuts (6) or a guaranteed single donut?

  12. Are you going to use openai for rl and keras when we come to deep reinforcement learning
    When will this playlist be finished.

  14. Is there a better reason than consolidating the total amount of stored data the reason we only store a single value per square? Why not store 4 values per square so you can store a value per direction you could go from the current spot. That way you could find/exploit the near black hole shortcut that the current algorithm is too scared to find.

  18. yay, this was actually better than most of the explanatory videos i have seen. thanks for providing us always with informative content, crash course. looking forward for more of these videos <3

  20. Not sure the kitchen metaphor works for me. Why is the bag more likely to contain donuts than the box? It sure looked like the kind of box that donuts come in to me.

  30. In the john green bot example, is the objective to find the shortest path or get the most points? What would getting more points even do, I feel like in that case exploration is best so that you can find the shortest path, exploiting only when racing another bot

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