Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Hands-On Intelligent Agents with OpenAI Gym

You're reading from   Hands-On Intelligent Agents with OpenAI Gym Your guide to developing AI agents using deep reinforcement learning

Arrow left icon
Product type Paperback
Published in Jul 2018
Publisher Packt
ISBN-13 9781788836579
Length 254 pages
Edition 1st Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Palanisamy Palanisamy
Author Profile Icon Palanisamy
Palanisamy
Arrow right icon
View More author details
Toc

Table of Contents (12) Chapters Close

Preface 1. Introduction to Intelligent Agents and Learning Environments FREE CHAPTER 2. Reinforcement Learning and Deep Reinforcement Learning 3. Getting Started with OpenAI Gym and Deep Reinforcement Learning 4. Exploring the Gym and its Features 5. Implementing your First Learning Agent - Solving the Mountain Car problem 6. Implementing an Intelligent Agent for Optimal Control using Deep Q-Learning 7. Creating Custom OpenAI Gym Environments - CARLA Driving Simulator 8. Implementing an Intelligent - Autonomous Car Driving Agent using Deep Actor-Critic Algorithm 9. Exploring the Learning Environment Landscape - Roboschool, Gym-Retro, StarCraft-II, DeepMindLab 10. Exploring the Learning Algorithm Landscape - DDPG (Actor-Critic), PPO (Policy-Gradient), Rainbow (Value-Based) 11. Other Books You May Enjoy

Summary

In this chapter, we explored the list of Gym environments available on your system, which you installed in the previous chapter, and then understood the naming conventions, or nomenclature, of the environments. We then revisited the agent-environment interaction (the RL loop) diagram and understood how the Gym environment provides the interfaces corresponding to each of the arrows we saw in the image. We then looked at a consolidated summary of the four values returned by the Gym environment's step() method in a tabulated, easy-to-understand format to reinforce your understanding of what they mean!

We also explored in detail the various types of spaces used in the Gym for the observation and action spaces, and we used a script to print out what spaces are used by an environment to understand the Gym environment interfaces better. In our next chapter, we will consolidate...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image