Book Image

Python Reinforcement Learning

By : Sudharsan Ravichandiran, Sean Saito, Rajalingappaa Shanmugamani, Yang Wenzhuo
Book Image

Python Reinforcement Learning

By: Sudharsan Ravichandiran, Sean Saito, Rajalingappaa Shanmugamani, Yang Wenzhuo

Overview of this book

Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. This Learning Path will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms. The Learning Path starts with an introduction to RL followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. You'll also work on various datasets including image, text, and video. This example-rich guide will introduce you to deep RL algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will gain experience in several domains, including gaming, image processing, and physical simulations. You'll explore TensorFlow and OpenAI Gym to implement algorithms that also predict stock prices, generate natural language, and even build other neural networks. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many of the recent advancements in RL. By the end of the Learning Path, you will have all the knowledge and experience needed to implement RL and deep RL in your projects, and you enter the world of artificial intelligence to solve various real-life problems. This Learning Path includes content from the following Packt products: • Hands-On Reinforcement Learning with Python by Sudharsan Ravichandiran • Python Reinforcement Learning Projects by Sean Saito, Yang Wenzhuo, and Rajalingappaa Shanmugamani
Table of Contents (27 chapters)
Title Page
About Packt
Contributors
Preface
Index

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.

Data parser

The data parser script is designed to help with the cleaning and preprocessing of our datasets. There are a number of dependencies in this script, such as pickle, codecs, re, OS, time, and numpy. This...