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
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Q-learning for FrozenLake
The whole example is in the Chapter05/02_frozenlake_q_learning.py file, and the difference is really minor. The most obvious change is to our value table. In the previous example, we kept the value of the state, so the key in the dictionary was just a state. Now we need to store values of the Q-function, which has two parameters: state and action, so the key in the value table is now a composite.
The second difference is in our calc_action_value function. We just don't need it anymore, as our action values are stored in the value table. Finally, the most important change in the code is in the agent's value_iteration method. Before, it was just a wrapper around the calc_action_value call, which did the job of Bellman approximation. ...
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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.
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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.