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You're reading from Reinforcement Learning with TensorFlow

Product typeBook

Published inApr 2018

Reading LevelIntermediate

PublisherPackt

ISBN-139781788835725

Edition1st Edition

Languages

Python

Tools

TensorFlow

Concepts

Reinforcement Learning

Author (1)

Sayon Dutta

Summary

In this chapter, we discussed real strategy games and why researchers from the AI community are trying to solve them. We also covered the complexity and properties of real strategy games and the different traditional AI approaches, such as case-based reasoning and online case-based planning to solve them and their drawbacks. We discussed the reason behind reinforcement learning being the perfect candidate for the problem and how it is successful in fulfilling the complexity and issues related to real-time strategy games where earlier traditional AI approaches failed. We also learnt about deep autoencoders and how they can be used to reduce the dimensionality of the input data and obtain a better representation of the input.

In the next chapter, we will cover the most famous topic that brought deep reinforcement learning into the limelight and made it the flag bearer of AI algorithms, that is, Alpha Go.

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Reinforcement Learning with TensorFlow

Published in: Apr 2018Publisher: PacktISBN-13: 9781788835725

Author (1)

Sayon Dutta

Sayon Dutta is an Artificial Intelligence researcher and developer. A graduate from IIT Kharagpur, he owns the software copyright for Mobile Irrigation Scheduler. At present, he is an AI engineer at Wissen Technology. He co-founded an AI startup Marax AI Inc., focused on AI-powered customer churn prediction. With over 2.5 years of experience in AI, he invests most of his time implementing AI research papers for industrial use cases, and weightlifting.
Read more about Sayon Dutta

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You're reading from Reinforcement Learning with TensorFlow

Summary

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Author (1)

Hands-On Reinforcement Learning with Python

Reinforcement learning is a self-evolving type of machine learning that takes us closer to achieving true artificial intelligence. This easy-to-follow guide explains everything from scratch using rich examples written in Python.

Hands-On Intelligent Agents with OpenAI Gym

TensorFlow Reinforcement Learning Quick Start Guide

Deep Reinforcement Learning with Python

Hands-On Q-Learning with Python

Python Reinforcement Learning Projects

PyTorch 1.x Reinforcement Learning Cookbook

TensorFlow 2 Reinforcement Learning Cookbook

Practical Reinforcement Learning

Reinforcement Learning Algorithms with Python

The Reinforcement Learning Workshop

Mastering Reinforcement Learning with Python

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