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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Value iteration in practice
The complete example is in Chapter05/01_frozenlake_v_learning.py. The central data structures in this example are as follows:
- Reward table: A dictionary with the composite key "source state" + "action" + "target state". The value is obtained from the immediate reward.
- Transitions table: A dictionary keeping counters of the experienced transitions. The key is the composite "state" + "action" and the value is another dictionary that maps the target state into a count of times that we've seen it. For example, if in state 0 we execute action 1 ten times, after three times it leads us to state 4 and after seven times to state 5. Entry with the key (0, 1) in this table will be a dict
{4: 3, 5: 7}. We use this table to estimate ...
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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.