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 various state-of-the-art architectures along with math

Book Description
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.

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

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.

Table of Contents
Introduction to Reinforcement Learning
Getting started with OpenAI and Tensorflow
Markov Decision process and Dynamic Programming
Gaming with Monte Carlo Tree Search
Temporal Difference Learning
Multi-Armed Bandit Problem
Deep Learning Fundamentals
Deep Learning and Reinforcement
Playing Doom With Deep Recurrent Q Network
Asynchronous Advantage Actor Critic Network
Policy Gradients and Optimization
Capstone Project – Car Racing using DQN
Current Research and Next Steps