June 2018
Intermediate to advanced
546 pages
13h 30m
English
book
Deep Reinforcement Learning Hands-On
by Oleg Vasilev, Maxim Lapan, Martijn van Otterlo, Mikhail Yurushkin, Basem O. F. Alijla
Content preview from Deep Reinforcement Learning Hands-On
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Theoretical background of the cross-entropy method
This section is optional and included for readers who are interested in why the method works. If you wish, you can refer to the original paper on cross-entropy, which will be given at the end of the section.
The basis of the cross-entropy method lies in the importance sampling theorem, which states this:
In our RL case, H(x) is a reward value obtained by some policy x and p(x) is a distribution of all possible policies. We don't want to maximize our reward by searching all possible policies, instead we want to find a way to approximate p(x)H(x) by q(x), iteratively minimizing the distance between ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.