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

AlphaGo Zero


We will cover AlphaGo Zero, the upgraded version of its predecessor before we finally get into some coding. The main features of AlphaGo Zero address some of the drawbacks of AlphaGo, including its dependency on a large corpus of games played by human experts.

The main differences between AlphaGo Zero and AlphaGo are the following:

  • AlphaGo Zero is trained solely with self-play reinforcement learning, meaning it does not rely on any human-generated data or supervision that is used to train AlphaGo
  • Policy and value networks are represented as one network with two heads rather than two separate ones
  • The input to the network is the board itself as an image, such as a 2D grid; the network does not rely on heuristics and instead uses the raw board state itself
  • In addition to finding the best move, Monte Carlo tree search is also used for policy iteration and evaluation; moreover, AlphaGo Zero does not conduct rollouts during a search

Training AlphaGo Zero

Since we don't use human-generated...