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Python Reinforcement Learning Projects

By : Sean Saito, Yang Wenzhuo , Shanmugamani

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Python Reinforcement Learning Projects

5 (1)

By: Sean Saito, Yang Wenzhuo , Shanmugamani

Buy this Book

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.

Preface

Who this book is for

What this book covers

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Free Chapter

Up and Running with Reinforcement Learning

Introduction to this book

What is reinforcement learning?

Deep learning

Summary

References

Balancing CartPole

OpenAI Gym

Markov models

Summary

Playing Atari Games

Introduction to Atari games

Building an Atari emulator

Data preparation

Deep Q-learning

Implementation of DQN

Experiments

Summary

Simulating Control Tasks

Introduction to control tasks

Deterministic policy gradient

Trust region policy optimization

Summary

Building Virtual Worlds in Minecraft

Introduction to the Minecraft environment

Data preparation

Asynchronous advantage actor-critic algorithm

Implementation of A3C

Experiments

Summary

Learning to Play Go

A brief introduction to Go

Monte Carlo tree search

AlphaGo

AlphaGo Zero

Implementing AlphaGo Zero

Summary

References

Creating a Chatbot

The background problem

Step-by-step guide

Summary

Generating a Deep Learning Image Classifier

Neural Architecture Search

Implementing NAS

Advantages of NAS

Summary

Predicting Future Stock Prices

Background problem

Data used

Step-by-step guide

Summary

Looking Ahead

The shortcomings of reinforcement learning

Upcoming developments in reinforcement learning

Summary

References

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Asynchronous advantage actor-critic algorithm

In the previous chapters, we discussed the DQN for playing Atari games and the use of the DPG and TRPO algorithms for continuous control tasks. Recall that DQN has the following architecture:

At each timestep , the agent observes the frame image and selects an action based on the current learned policy. The emulator (the Minecraft environment) executes this action and returns the next frame image and the corresponding reward . The quadruplet is then stored in the experience memory and is taken as a sample for training the Q-network by minimizing the empirical loss function via stochastic gradient descent.

Deep reinforcement learning algorithms based on experience replay have achieved unprecedented success in playing Atari games. However, experience replay has several disadvantages: