Python Reinforcement Learning: Solve complex real-world problems by mastering reinforcement learning algorithms using OpenAI Gym and TensorFlow (English Edition)

Apply modern reinforcement learning and deep reinforcement learning methods using Python and its powerful libraries

Key Features

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 the power of modern Python libraries to gain confidence in building self-trained applications

Book Description

Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. This Learning Path will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms.

The Learning Path starts with an introduction to RL followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. You'll also work on various datasets including image, text, and video. This example-rich guide will introduce you to deep RL algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will gain experience in several domains, including gaming, image processing, and physical simulations. You'll explore TensorFlow and OpenAI Gym to implement algorithms that also predict stock prices, generate natural language, and even build other neural networks. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many of the recent advancements in RL.

By the end of the Learning Path, you will have all the knowledge and experience needed to implement RL and deep RL in your projects, and you enter the world of artificial intelligence to solve various real-life problems.

This Learning Path includes content from the following Packt products:

Hands-On Reinforcement Learning with Python by Sudharsan Ravichandiran

Python Reinforcement Learning Projects by Sean Saito, Yang Wenzhuo, and Rajalingappaa Shanmugamani

What you will learn

Train an agent to walk using OpenAI Gym and TensorFlow

Solve multi-armed-bandit problems using various algorithms

Build intelligent agents using the DRQN algorithm to play the Doom game

Teach your agent to play Connect4 using AlphaGo Zero

Defeat Atari arcade games using the value iteration method

Discover how to deal with discrete and continuous action spaces in various environments

Who this book is for

If you’re an ML/DL enthusiast interested in AI and want to explore RL and deep RL from scratch, this Learning Path is for you. Prior knowledge of linear algebra is expected. Table of Contents

Introduction to Reinforcement Learning

Getting Started with OpenAI and TensorFlow

The Markov Decision Process and Dynamic Programming

Gaming with Monte Carlo Methods

Temporal Difference Learning

Multi-Armed Bandit Problem

Playing Atari Games

Atari Games with Deep Q Network

Playing Doom with a Deep Recurrent Q Network

The Asynchronous Advantage Actor Critic Network

Policy Gradients and Optimization

Balancing CartPole

Simulating Control Tasks

Building Virtual Worlds in Minecraft

Learning to Play Go

Creating a Chatbot

Generating a Deep Learning Image Classifier

Predicting Future Stock Prices

Capstone Project - Car Racing Using DQN

Looking Ahead

Python Reinforcement Learning: Solve complex real-world problems by mastering reinforcement learning algorithms using OpenAI Gym and TensorFlow (English Edition) EPUB, PDF, MOBI, AZW3, TXT, FB2, DjVu, Kindle电子书免费下载。