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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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The background problem


Many chatbots are created with regular machine learning natural language processing algorithms, and these focus on immediate responses. A new concept is to create chatbots with the use of deep reinforcement learning. This would mean that the future implications of our immediate responses would be considered to maintain coherence.

In this chapter, you will learn how to apply deep reinforcement learning to natural language processing. Our reward function will be a future-looking function, and you will learn how to think probabilistically through the creation of this function.

Dataset

The dataset that we will use mainly consists of conversations from selected movies. This dataset will help to stimulate and understand conversational methods in the chatbot. Also, there are movie lines, which are essentially the same as the movie conversations, albeit shorter exchanges between people. Other data sets that will be used include some containing movie titles, movie characters,...

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