July 2018
Intermediate to advanced
254 pages
8h 12m
English
Content preview from Hands-On Intelligent Agents with OpenAI Gym
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Summary
We started this chapter with the grand goal of developing intelligent learning agents that can achieve great scores in Atari games. We made incremental progress towards it by implementing several techniques to improve upon the Q-learner that we developed in the previous chapter. We first started with learning how we can use a neural network to approximate the Q action-value function and made our learning concrete by practically implementing a shallow neural network to solve the famous Cart Pole problem. We then implemented experience memory and experience replay that enables the agent to learn from (mini) randomly sampled batches of experiences that helped in improving the performance by breaking the correlations between the agent's ...
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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.