An Introduction to Deep Reinforcement Learning

This book PDF is perfect for those who love Electronic Books genre, written by Vincent Francois-Lavet and published by Foundations and Trends (R) in Machine Learning which was released on 20 December 2018 with total hardcover pages 156. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related An Introduction to Deep Reinforcement Learning books below.

An Introduction to Deep Reinforcement Learning
An Introduction to Deep Reinforcement Learning
Author : Vincent Francois-Lavet
File Size : 41,5 Mb
Publisher : Foundations and Trends (R) in Machine Learning
Language : English
Release Date : 20 December 2018
ISBN : 1680835386
Pages : 156 pages
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An Introduction to Deep Reinforcement Learning by Vincent Francois-Lavet Book PDF Summary

Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has recently been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine. Deep RL opens up many new applications in domains such as healthcare, robotics, smart grids, finance, and many more. This book provides the reader with a starting point for understanding the topic. Although written at a research level it provides a comprehensive and accessible introduction to deep reinforcement learning models, algorithms and techniques. Particular focus is on the aspects related to generalization and how deep RL can be used for practical applications. Written by recognized experts, this book is an important introduction to Deep Reinforcement Learning for practitioners, researchers and students alike.

An Introduction to Deep Reinforcement Learning

An Introduction to Deep Reinforcement Learning by Vincent Francois-Lavet,Peter Henderson,Riashat Islam,Marc G. Bellemare,Joelle Pineau

Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has recently been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine. Deep RL opens up many new applications in domains such as

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An Introduction to Deep Reinforcement Learning

An Introduction to Deep Reinforcement Learning by Vincent François-Lavet

Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has been able to solve a wide range of complex decisionmaking tasks that were previously out of reach for a machine. Thus, deep RL opens up many new applications in domains such as

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Reinforcement Learning second edition

Reinforcement Learning second edition by Richard S. Sutton,Andrew G. Barto

The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the

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Foundations of Deep Reinforcement Learning

Foundations of Deep Reinforcement Learning by Laura Graesser,Wah Loon Keng

The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the past decade deep RL has achieved remarkable results on a range of problems, from single

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Deep Reinforcement Learning

Deep Reinforcement Learning by Hao Dong,Zihan Ding,Shanghang Zhang

Deep reinforcement learning (DRL) is the combination of reinforcement learning (RL) and deep learning. It has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine, and famously contributed to the success of AlphaGo. Furthermore, it opens up numerous new

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Introduction to Deep Learning

Introduction to Deep Learning by Eugene Charniak

A project-based guide to the basics of deep learning. This concise, project-driven guide to deep learning takes readers through a series of program-writing tasks that introduce them to the use of deep learning in such areas of artificial intelligence as computer vision, natural-language processing, and reinforcement learning. The author, a

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Grokking Deep Reinforcement Learning

Grokking Deep Reinforcement Learning by Miguel Morales

Grokking Deep Reinforcement Learning uses engaging exercises to teach you how to build deep learning systems. This book combines annotated Python code with intuitive explanations to explore DRL techniques. You’ll see how algorithms function and learn to develop your own DRL agents using evaluative feedback. Summary We all learn

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This book starts by presenting the basics of reinforcement learning using highly intuitive and easy-to-understand examples and applications, and then introduces the cutting-edge research advances that make reinforcement learning capable of out-performing most state-of-art systems, and even humans in a number of applications. The book not only equips readers with

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