July 2018
Intermediate to advanced
254 pages
8h 12m
English
Content preview from Hands-On Intelligent Agents with OpenAI Gym
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Implementing an Intelligent Agent for Optimal Control using Deep Q-Learning
In the previous chapter, we implemented an intelligent agent that used Q-learning to solve the Mountain Car problem from scratch in about seven minutes on a dual-core laptop CPU. In this chapter, we will implement an advanced version of Q-learning called deep Q-learning, which can be used to solve several discrete control problems that are much more complex than the Mountain Car problem. Discrete control problems are (sequential) decision-making problems in which the action space is discretized into a finite number of values. In the previous chapter, the learning agent used a 2-dimensional state-space vector as the input, which contained the information about the ...
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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.