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Hands-On Reinforcement Learning with Python

You're reading from Hands-On Reinforcement Learning with Python

Product type Book

Published in Jun 2018

Publisher Packt

ISBN-13 9781788836524

Pages 318 pages

Edition 1st Edition

Languages

Concepts

Reinforcement Learning

Author (1):

Sudharsan Ravichandiran

Table of Contents (16) Chapters

Preface

1. Introduction to Reinforcement Learning

2. Getting Started with OpenAI and TensorFlow

3. The Markov Decision Process and Dynamic Programming

4. Gaming with Monte Carlo Methods

5. Temporal Difference Learning

6. Multi-Armed Bandit Problem

7. Deep Learning Fundamentals

8. Atari Games with Deep Q Network

9. Playing Doom with a Deep Recurrent Q Network

10. The Asynchronous Advantage Actor Critic Network

11. Policy Gradients and Optimization

12. Capstone Project – Car Racing Using DQN

13. Recent Advancements and Next Steps

14. Assessments

15. Other Books You May Enjoy

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Driving up a mountain with A3C

Let's understand A3C with a mountain car example. Our agent is the car and it is placed between two mountains. The goal of our agent is to drive up the mountain on the right. However, the car can't drive up the mountain in one pass; it has to drive up back and forth to build the momentum. A high reward will be assigned if our agent spends less energy on driving up. Credits for the code used in this section goes to Stefan Boschenriedter (https://github.com/stefanbo92/A3C-Continuous). The environment is shown as follows:

Okay, let's get to the coding! The complete code is available as the Jupyter notebook with an explanation here (https://github.com/sudharsan13296/Hands-On-Reinforcement-Learning-With-Python/blob/master/10.%20Aysnchronous%20Advantage%20Actor%20Critic%20Network/10.5%20Drive%20up%20the%20Mountain%20Using%20A3C.ipynb...

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Authors (1)

Sudharsan Ravichandiran

Sudharsan Ravichandiran is a data scientist and artificial intelligence enthusiast. He holds a Bachelors in Information Technology from Anna University. His area of research focuses on practical implementations of deep learning and reinforcement learning including natural language processing and computer vision. He is an open-source contributor and loves answering questions on Stack Overflow.

See other products by Sudharsan Ravichandiran

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