July 2018
Intermediate to advanced
254 pages
8h 12m
English
Content preview from Hands-On Intelligent Agents with OpenAI Gym
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Monte Carlo learning and temporal difference learning
At this point, we understand that it is very useful for an agent to learn the state value function 
, which informs the agent about the long-term value of being in state 
so that the agent can decide if it is a good state to be in or not. The Monte Carlo (MC) and Temporal Difference (TD) learning methods enable an agent to learn that!
The goal of MC and TD learning is to learn the value functions from the agent's experience as the agent follows its policy .
The following table summarizes 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.