June 2018
Intermediate to advanced
318 pages
9h 24m
English
Content preview from Hands-On Reinforcement Learning with Python
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Summary
In this chapter, we learned a different model-free learning algorithm that overcame the limitations of the Monte Carlo methods. We saw both prediction and control methods. In TD prediction, we updated the state-value of a state based on the next state. In terms of the control methods, we saw two different algorithms: Q learning and SARSA.
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
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.