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Getting Started with OpenAI and TensorFlow

OpenAI is a non-profit, open source artificial intelligence (AI) research company founded by Elon Musk and Sam Altman that aims to build a general AI. They are sponsored by top industry leaders and top-notch companies. OpenAI comes in two flavors, Gym and Universe, using which we can simulate realistic environments, build reinforcement learning (RL) algorithms, and test our agents in those environments. TensorFlow is an open source machine learning library by Google that is extensively used for numerical computation. We will use OpenAI and TensorFlow for building and evaluating powerful RL algorithms in the upcoming chapters.

In this chapter, you will learn about the following:

Setting up your machine by installing Anaconda, Docker, OpenAI Gym, and Universe and TensorFlow
Simulating an environment using OpenAI Gym and Universe
Training...

Key benefits

•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

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.

Who is this book 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

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