October 2019
Intermediate to advanced
366 pages
12h 4m
English
Content preview from Reinforcement Learning Algorithms with Python
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Results
For off-policy algorithms (such as DQN), n-step learning works well with small values of n. In DQN, it has been shown that the algorithm works well with values of n between 2 and 4, leading to improvements in a wide range of Atari games.
In the following graph, the results of our implementation are visible. We tested DQN with a three-step return. From the results, we can see that it requires more time before taking off. Afterward, it has a steeper learning curve but with an overall similar learning curve compared to DQN:
Figure 5.11. A plot of the mean test total reward. The three-step DQN values are plotted in violet and the DQN ...
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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.