Deep Reinforcement Learning Hands-On

Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more, 2nd Edition

New edition of the bestselling guide to deep reinforcement learning and how it's used to solve complex real-world problems. Revised and expanded to include multi-agent methods, discrete optimization, RL in robotics, advanced exploration techniques, and more

Key Features

Second edition of the bestselling introduction to deep reinforcement learning, expanded with six new chapters
Learn advanced exploration techniques including noisy networks, pseudo-count, and network distillation methods
Apply RL methods to cheap hardware robotics platforms

Book DescriptionDeep Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques. Les mer
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Paperback
Legg i
Paperback
Legg i
Vår pris: 466,-

(Paperback) Fri frakt!
Leveringstid: Sendes innen 21 dager
På grunn av Brexit-tilpasninger og tiltak for å begrense covid-19 kan det dessverre oppstå forsinket levering.

Om boka

New edition of the bestselling guide to deep reinforcement learning and how it's used to solve complex real-world problems. Revised and expanded to include multi-agent methods, discrete optimization, RL in robotics, advanced exploration techniques, and more

Key Features

Second edition of the bestselling introduction to deep reinforcement learning, expanded with six new chapters
Learn advanced exploration techniques including noisy networks, pseudo-count, and network distillation methods
Apply RL methods to cheap hardware robotics platforms

Book DescriptionDeep Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques. It provides you with an introduction to the fundamentals of RL, along with the hands-on ability to code intelligent learning agents to perform a range of practical tasks.

With six new chapters devoted to a variety of up-to-the-minute developments in RL, including discrete optimization (solving the Rubik's Cube), multi-agent methods, Microsoft's TextWorld environment, advanced exploration techniques, and more, you will come away from this book with a deep understanding of the latest innovations in this emerging field.

In addition, you will gain actionable insights into such topic areas as deep Q-networks, policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. You will also discover how to build a real hardware robot trained with RL for less than $100 and solve the Pong environment in just 30 minutes of training using step-by-step code optimization.

In short, Deep Reinforcement Learning Hands-On, Second Edition, is your companion to navigating the exciting complexities of RL as it helps you attain experience and knowledge through real-world examples.

What you will learn

Understand the deep learning context of RL and implement complex deep learning models
Evaluate RL methods including cross-entropy, DQN, actor-critic, TRPO, PPO, DDPG, D4PG, and others
Build a practical hardware robot trained with RL methods for less than $100
Discover Microsoft's TextWorld environment, which is an interactive fiction games platform
Use discrete optimization in RL to solve a Rubik's Cube
Teach your agent to play Connect 4 using AlphaGo Zero
Explore the very latest deep RL research on topics including AI chatbots
Discover advanced exploration techniques, including noisy networks and network distillation techniques

Who this book is forSome fluency in Python is assumed. Sound understanding of the fundamentals of deep learning will be helpful. This book is an introduction to deep RL and requires no background in RL

Fakta

Innholdsfortegnelse

Table of Contents

What Is Reinforcement Learning?
OpenAI Gym
Deep Learning with PyTorch
The Cross-Entropy Method
Tabular Learning and the Bellman Equation
Deep Q-Networks
Higher-Level RL libraries
DQN Extensions
Ways to Speed up RL
Stocks Trading Using RL
Policy Gradients - an Alternative
The Actor-Critic Method
Asynchronous Advantage Actor-Critic
Training Chatbots with RL
The TextWorld environment
Web Navigation
Continuous Action Space
RL in Robotics
Trust Regions - PPO, TRPO, ACKTR, and SAC
Black-Box Optimization in RL
Advanced exploration
Beyond Model-Free - Imagination
AlphaGo Zero
RL in Discrete Optimisation
Multi-agent RL

Om forfatteren

Maxim Lapan is a deep learning enthusiast and independent researcher. His background and 15 years' work expertise as a software developer and a systems architect lies from low-level Linux kernel driver development to performance optimization and design of distributed applications working on thousands of servers. With vast work experiences in big data, machine learning, and large parallel distributed HPC and non-HPC systems, he is able to explain a number of complicated concepts in simple words and vivid examples. His current