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Python Reinforcement Learning Projects

You're reading from   Python Reinforcement Learning Projects Eight hands-on projects exploring reinforcement learning algorithms using TensorFlow

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Product type Paperback
Published in Sep 2018
Publisher Packt
ISBN-13 9781788991612
Length 296 pages
Edition 1st Edition
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Authors (3):
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Sean Saito Sean Saito
Author Profile Icon Sean Saito
Sean Saito
Rajalingappaa Shanmugamani Rajalingappaa Shanmugamani
Author Profile Icon Rajalingappaa Shanmugamani
Rajalingappaa Shanmugamani
Yang Wenzhuo Yang Wenzhuo
Author Profile Icon Yang Wenzhuo
Yang Wenzhuo
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Toc

Step-by-step guide

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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