July 2018
Intermediate to advanced
254 pages
8h 12m
English
Content preview from Hands-On Intelligent Agents with OpenAI Gym
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The likelihood ratio trick
The policy represented by 
is assumed to be a differentiable function whenever it is non-zero, but computing the gradient of the policy with respect to theta, 
, may not be straightforward. We can multiply and divide by policy 
on both sides to get the following:
From calculus, we know that the gradient of the log of ...
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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.