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Python Reinforcement Learning Projects

Python Reinforcement Learning Projects

By : Sean Saito, Yang Wenzhuo , Shanmugamani
5 (1)
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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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Monte Carlo tree search


In games such as Go and chess, players have perfect information, meaning they have access to the full game state (the board and the positions of the pieces). Moreover, there lacks an element of chance that can affect the game state; only the players' decisions can affect the board. Such games are also referred to as perfect-information games. In perfect-information games, it is theoretically possible to enumerate all possible game states. As discussed earlier, this would look such as a tree, where each child node (a game state) is a possible outcome of the parent. In two-player games, alternating levels of this tree represent moves produced by the two competitors. Finding the best possible move for a given state is simply a matter of traversing the tree and finding which sequence of moves leads to a win. We can also store the value, or the expected outcome or reward (a win or a loss) of a given state, at each node.

However, constructing a perfect tree is impractical...

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