Book Image

Python Reinforcement Learning

By : Sudharsan Ravichandiran, Sean Saito, Rajalingappaa Shanmugamani, Yang Wenzhuo
Book Image

Python Reinforcement Learning

By: Sudharsan Ravichandiran, Sean Saito, Rajalingappaa Shanmugamani, Yang Wenzhuo

Overview of this book

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
Table of Contents (27 chapters)
Title Page
About Packt
Contributors
Preface
Index

OpenAI Gym


OpenAI is a non-profit organization dedicated to researching artificial intelligence. Visit https://openai.com for more information about the mission of OpenAI. The technologies developed by OpenAI are free for anyone to use.

Gym

Gym provides a toolkit to benchmark AI-based tasks. The interface is easy to use. The goal is to enable reproducible research. Visit https://gym.openai.com for more information about Gym. An agent can be taught inside of the gym, and learn activities such as playing games or walking. An environment is a library of problems.

The standard set of problems presented in the gym are as follows:

  • CartPole
  • Pendulum
  • Space Invaders
  • Lunar Lander
  • Ant
  • Mountain Car
  • Acrobot
  • Car Racing
  • Bipedal Walker

Any algorithm can work out in the gym by training for these activities. All of the problems have the same interface. Therefore, any general reinforcement learning algorithm can be used through the interface.

Installation 

The primary interface of the gym is used through Python. Once you...