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Hands-On Reinforcement Learning with Python

You're reading from Hands-On Reinforcement Learning with Python Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow

Product type Paperback

Published in Jun 2018

Publisher Packt

ISBN-13 9781788836524

Length 318 pages

Edition 1st Edition

Languages

Python

Tools

Deep Reinforcement Learning

Concepts

Deep Reinforcement Learning

Author (1):

Sudharsan Ravichandiran

View More author details

Table of Contents (16) Chapters

Preface

1. Introduction to Reinforcement Learning

2. Getting Started with OpenAI and TensorFlow FREE CHAPTER

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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Deep deterministic policy gradient

In Chapter 8, Atari Games with Deep Q Network, we looked at how DQN works and we applied DQNs to play Atari games. However, those are discrete environments where we have a finite set of actions. Think of a continuous environment space like training a robot to walk; in those environments it is not feasible to apply Q learning because finding a greedy policy will require a lot of optimization at each and every step. Even if we make this continuous environment discrete, we might lose important features and end up with a huge set of action spaces. It is difficult to attain convergence when we have a huge action space.

So we use a new architecture called Actor Critic with two networks—Actor and Critic. The Actor Critic architecture combines the policy gradient and state action value functions. The role of the Actor network is to determine the...

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Authors (1)

Sudharsan Ravichandiran

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.

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