Foundations Of Deep Reinforcement Learning

Download Foundations Of Deep Reinforcement Learning full books in PDF, epub, and Kindle. Read online free Foundations Of Deep Reinforcement Learning ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!

Foundations of Deep Reinforcement Learning

Foundations of Deep Reinforcement Learning
Author :
Publisher : Pearson Professional
Total Pages : 0
Release :
ISBN-10 : 0135172381
ISBN-13 : 9780135172384
Rating : 4/5 (384 Downloads)

Book Synopsis Foundations of Deep Reinforcement Learning by : Laura Graesser

Download or read book Foundations of Deep Reinforcement Learning written by Laura Graesser and published by Pearson Professional. This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 and multiplayer games--such as Go, Atari games, and DotA 2--to robotics. Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work. Understand each key aspect of a deep RL problem Explore policy- and value-based algorithms, including REINFORCE, SARSA, DQN, Double DQN, and Prioritized Experience Replay (PER) Delve into combined algorithms, including Actor-Critic and Proximal Policy Optimization (PPO) Understand how algorithms can be parallelized synchronously and asynchronously Run algorithms in SLM Lab and learn the practical implementation details for getting deep RL to work Explore algorithm benchmark results with tuned hyperparameters Understand how deep RL environments are designed This guide is ideal for both computer science students and software engineers who are familiar with basic machine learning concepts and have a working understanding of Python.


Foundations of Deep Reinforcement Learning Related Books

Foundations of Deep Reinforcement Learning
Language: en
Pages: 0
Authors: Laura Graesser
Categories: Artificial intelligence
Type: BOOK - Published: 2020 - Publisher: Pearson Professional

DOWNLOAD EBOOK

The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice Deep reinforcement learning (deep RL) combines deep learning and
Deep Reinforcement Learning
Language: en
Pages: 526
Authors: Hao Dong
Categories: Computers
Type: BOOK - Published: 2020-06-29 - Publisher: Springer Nature

DOWNLOAD EBOOK

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 decisio
An Introduction to Deep Reinforcement Learning
Language: en
Pages: 156
Authors: Vincent Francois-Lavet
Categories:
Type: BOOK - Published: 2018-12-20 - Publisher: Foundations and Trends (R) in Machine Learning

DOWNLOAD EBOOK

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 r
Grokking Deep Reinforcement Learning
Language: en
Pages: 470
Authors: Miguel Morales
Categories: Computers
Type: BOOK - Published: 2020-10-15 - Publisher: Simon and Schuster

DOWNLOAD EBOOK

Grokking Deep Reinforcement Learning uses engaging exercises to teach you how to build deep learning systems. This book combines annotated Python code with intu
Deep Reinforcement Learning in Action
Language: en
Pages: 381
Authors: Alexander Zai
Categories: Computers
Type: BOOK - Published: 2020-04-28 - Publisher: Manning Publications

DOWNLOAD EBOOK

Summary Humans learn best from feedback—we are encouraged to take actions that lead to positive results while deterred by decisions with negative consequences