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Python Reinforcement Learning Projects

Python Reinforcement Learning Projects

By : Sean Saito, Yang Wenzhuo , Shanmugamani
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Python Reinforcement Learning Projects

Python Reinforcement Learning Projects

5 (1)
By: Sean Saito, Yang Wenzhuo , Shanmugamani

Overview of this book

Reinforcement learning is one of the most exciting and rapidly growing fields in machine learning. This is due to the many novel algorithms developed and incredible results published in recent years. In this book, you will learn about the core concepts of RL including Q-learning, policy gradients, Monte Carlo processes, and several deep reinforcement learning algorithms. As you make your way through the book, you'll work on projects with datasets of various modalities including image, text, and video. You will gain experience in several domains, including gaming, image processing, and physical simulations. You'll explore technologies such as TensorFlow and OpenAI Gym to implement deep learning reinforcement learning algorithms that also predict stock prices, generate natural language, and even build other neural networks. By the end of this book, you will have hands-on experience with eight reinforcement learning projects, each addressing different topics and/or algorithms. We hope these practical exercises will provide you with better intuition and insight about the field of reinforcement learning and how to apply its algorithms to various problems in real life.
Table of Contents (12 chapters)
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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...

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