idavidrein

3 ms·

1.
A Technical Introduction to Reinforcement Learning(notion.so)
7 points by idavidrein 6y ago|1 comments
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by idavidrein 6y ago
Hey HN, I wrote an intro to reinforcement learning (RL) tutorial that tries to balance technical depth and high-level breadth. I hope this can be a useful intro to the field for people who are interested in RL, but don't want to read t
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by idavidrein 6y ago
You're not thinking broadly enough about the utility maximization framework. What's wrong with just including bread baking, hanging out with friends, etc. as things you find valuable, and then try and maximize those jointly with t
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by idavidrein 6y ago
that's a fair point, but vision is such a high-dimensional and dynamic input that my feeling is the last 20% of super-fast autofocus, auto color balancing, high dynamic range, removing motion-blur, etc. will be very difficult to improv
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by idavidrein 6y ago
yeah, it's possible that the stuff doesn't look very good, but my guess (maybe my hope?) is that it's too cluttering or through careful analysis could reveal IP about their predictive algos
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by idavidrein 6y ago
given the rate of improvement in camera tech, I'd say we are very far away from passthrough to be usable
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by idavidrein 6y ago
that's a fair point, but it seems reasonable to me that they would separate the sensory input and the predictive/higher-level aspects of their modeling. For example, we know for a fact that they must be doing tons of prediction fo
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by idavidrein 6y ago
well the ideal cheap ride-sharing AV world would have much more spread out cities, no?
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by idavidrein 6y ago
I agree with a significant amount of your point, but with regard to object permanence, I would guess that they have prediction algorithms that don't only rely on the current-time perception, so if something blips out of sight for a sec