April 2019
Intermediate to advanced
212 pages
5h 34m
English
Content preview from Hands-On Q-Learning with Python
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SARSA versus Q-learning – on-policy or off?
Similar to Q-learning, SARSA is a model-free RL method that does not explicitly learn the agent's policy function.
The primary difference between SARSA and Q-learning is that SARSA is an on-policy method while Q-learning is an off-policy method. The effective difference between the two algorithms happens in the step where the Q-table is updated. Let's discuss what that means with some examples:
Monte Carlo tree search (MCTS) is a type of model-based RL. We won't be discussing it in detail here, but it's useful to explore further as a contrast to model-free RL algorithms. Briefly, in model-based ...
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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.