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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A2C on Pong
In the previous chapter, we saw a (not very successful) attempt to solve our favorite Pong environment with PG. Let's try it again with the actor-critic method at hand.
GAMMA = 0.99 LEARNING_RATE = 0.001 ENTROPY_BETA = 0.01 BATCH_SIZE = 128 NUM_ENVS = 50 REWARD_STEPS = 4 CLIP_GRAD = 0.1
We're starting, as usual, by defining hyperparameters (imports are omitted). These values are not tuned, as we'll do this in the next section of this chapter. We have one new value here: CLIP_GRAD. This hyperparameter is specifying the threshold for gradient clipping, which, basically, prevents our gradients at optimization stage from becoming too large and pushing our policy too far. Clipping is implemented using the PyTorch functionality, but the idea ...
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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.