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Hands-On Reinforcement Learning with Python电子书

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6.2

作者：Sudharsan Ravichandiran

出版社：Packt Publishing

出版时间：2018-06-28

字数：32.1万

所属分类：进口书 > 外文原版书 > 电脑/网络

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A hands-on guide enriched with examples to master deep reinforcement learning algorithms with Python About This Book ? Your entry point into the world of artificial intelligence using the power of Python ? An example-rich guide to master various RL and DRL algorithms ? Explore various state-of-the-art architectures along with math Who This Book Is For If you’re a machine learning developer or deep learning enthusiast interested in artificial intelligence and want to learn about reinforcement learning from scratch, this book is for you. Some knowledge of linear algebra, calculus, and the Python programming language will help you understand the concepts covered in this book. What You Will Learn ? Understand the basics of reinforcement learning methods, algorithms, and elements ? Train an agent to walk using OpenAI Gym and Tensorflow ? Understand the Markov Decision Process, Bellman’s optimality, and TD learning ? Solve multi-armed-bandit problems using various algorithms ? Master deep learning algorithms, such as RNN, LSTM, and CNN with applications ? Build intelligent agents using the DRQN algorithm to play the Doom game ? Teach agents to play the Lunar Lander game using DDPG ? Train an agent to win a car racing game using dueling DQN In Detail Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. Hands-On Reinforcement learning with Python will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms. The book starts with an introduction to Reinforcement Learning followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms and concepts, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. This example-rich guide will introduce you to deep reinforcement learning algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many more of the recent advancements in reinforcement learning. By the end of the book, you will have all the knowledge and experience needed to implement reinforcement learning and deep reinforcement learning in your projects, and you will be all set to enter the world of artificial intelligence. Style and approach This is a hands-on book designed to further expand your machine learning skills by understanding reinforcement to deep reinforcement learning algorithms with applications in Python.

目录展开

Title Page

Hands-On Reinforcement Learning with Python

Dedication

Packt Upsell

Why subscribe?

PacktPub.com

Contributors

About the author

About the reviewers

Packt is searching for authors like you

Preface

Who this book is for

What this book covers

To get the most out of this book

Download the example code files

Download the color images

Conventions used

Get in touch

Reviews

Introduction to Reinforcement Learning

What is RL?

RL algorithm

How RL differs from other ML paradigms

Elements of RL

Agent

Policy function

Value function

Model

Agent environment interface

Types of RL environment

Deterministic environment

Stochastic environment

Fully observable environment

Partially observable environment

Discrete environment

Continuous environment

Episodic and non-episodic environment

Single and multi-agent environment

RL platforms

OpenAI Gym and Universe

DeepMind Lab

RL-Glue

Project Malmo

ViZDoom

Applications of RL

Education

Medicine and healthcare

Manufacturing

Inventory management

Finance

Natural Language Processing and Computer Vision

Summary

Questions

Further reading

Getting Started with OpenAI and TensorFlow

Setting up your machine

Installing Anaconda

Installing Docker

Installing OpenAI Gym and Universe

Common error fixes

OpenAI Gym

Basic simulations

Training a robot to walk

OpenAI Universe

Building a video game bot

TensorFlow

Variables, constants, and placeholders

Variables

Constants

Placeholders

Computation graph

Sessions

TensorBoard

Adding scope

Summary

Questions

Further reading

The Markov Decision Process and Dynamic Programming

The Markov chain and Markov process

Markov Decision Process

Rewards and returns

Episodic and continuous tasks

Discount factor

The policy function

State value function

State-action value function (Q function)

The Bellman equation and optimality

Deriving the Bellman equation for value and Q functions

Solving the Bellman equation

Dynamic programming

Value iteration

Policy iteration

Solving the frozen lake problem

Value iteration

Policy iteration

Summary

Questions

Further reading

Gaming with Monte Carlo Methods

Monte Carlo methods

Estimating the value of pi using Monte Carlo

Monte Carlo prediction

First visit Monte Carlo

Every visit Monte Carlo

Let's play Blackjack with Monte Carlo

Monte Carlo control

Monte Carlo exploration starts

On-policy Monte Carlo control

Off-policy Monte Carlo control

Summary

Questions

Further reading

Temporal Difference Learning

TD learning

TD prediction

TD control

Q learning

Solving the taxi problem using Q learning

SARSA

Solving the taxi problem using SARSA

The difference between Q learning and SARSA

Summary

Questions

Further reading

Multi-Armed Bandit Problem

The MAB problem

The epsilon-greedy policy

The softmax exploration algorithm

The upper confidence bound algorithm

The Thompson sampling algorithm

Applications of MAB

Identifying the right advertisement banner using MAB

Contextual bandits

Summary

Questions

Further reading

Deep Learning Fundamentals

Artificial neurons

ANNs

Input layer

Hidden layer

Output layer

Activation functions

Deep diving into ANN

Gradient descent

Neural networks in TensorFlow

RNN

Backpropagation through time

Long Short-Term Memory RNN

Generating song lyrics using LSTM RNN

Convolutional neural networks

Convolutional layer

Pooling layer

Fully connected layer

CNN architecture

Classifying fashion products using CNN

Summary

Questions

Further reading

Atari Games with Deep Q Network

What is a Deep Q Network?

Architecture of DQN

Convolutional network

Experience replay

Target network

Clipping rewards

Understanding the algorithm

Building an agent to play Atari games

Double DQN

Prioritized experience replay

Dueling network architecture

Summary

Questions

Further reading

Playing Doom with a Deep Recurrent Q Network

DRQN

Architecture of DRQN

Training an agent to play Doom

Basic Doom game

Doom with DRQN

DARQN

Architecture of DARQN

Summary

Questions

Further reading

The Asynchronous Advantage Actor Critic Network

The Asynchronous Advantage Actor Critic

The three As

The architecture of A3C

How A3C works

Driving up a mountain with A3C

Visualization in TensorBoard

Summary

Questions

Further reading

Policy Gradients and Optimization

Policy gradient

Lunar Lander using policy gradients

Deep deterministic policy gradient

Swinging a pendulum

Trust Region Policy Optimization

Proximal Policy Optimization

Summary

Questions

Further reading

Capstone Project – Car Racing Using DQN

Environment wrapper functions

Dueling network

Replay memory

Training the network

Car racing

Summary

Questions

Further reading

Recent Advancements and Next Steps

Imagination augmented agents

Learning from human preference

Deep Q learning from demonstrations

Hindsight experience replay

Hierarchical reinforcement learning

MAXQ Value Function Decomposition

Inverse reinforcement learning

Summary

Questions

Further reading

Assessments

Chapter 1

Chapter 2

Chapter 3

Chapter 4

Chapter 5

Chapter 6

Chapter 7

Chapter 8

Chapter 9

Chapter 10

Chapter 11

Chapter 12

Chapter 13

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