Our solution will use modeling and will focus on the future direction of a dialogue agent, so as to generate coherent and interesting dialogue. The model will simulate the dialogue between two virtual agents, with the use of policy gradient methods. These methods are designed to reward the sequences of interaction that display three important properties of conversation: informativeness (non-repeating turns), high coherence, and simplicity in answering (this is related to the forward-looking function). In our solution, an action will be defined as the dialogue or communication utterance that the chatbot generates. Also, a state will be defined as the two previous interaction turns. In order to achieve all of this, we will use the scripts in the following sections.
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Python Reinforcement Learning Projects
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Python Reinforcement Learning Projects
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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)
Preface
Free Chapter
Up and Running with Reinforcement Learning
Balancing CartPole
Playing Atari Games
Simulating Control Tasks
Building Virtual Worlds in Minecraft
Learning to Play Go
Creating a Chatbot
Generating a Deep Learning Image Classifier
Predicting Future Stock Prices
Looking Ahead
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