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Hands-On Intelligent Agents with OpenAI Gym

You're reading from  Hands-On Intelligent Agents with OpenAI Gym

Product type Book
Published in Jul 2018
Publisher Packt
ISBN-13 9781788836579
Pages 254 pages
Edition 1st Edition
Languages
Author (1):
Palanisamy P Palanisamy P
Profile icon Palanisamy P

Table of Contents (12) Chapters

Preface 1. Introduction to Intelligent Agents and Learning Environments 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 looked at several interesting and valuable learning environments, saw how their interfaces are set up, and even got hands-on with those environments using the quickstart guides for each environment and the setup scripts available in the book's code repository. We first looked at environments that have interfaces compatible with the OpenAI Gym interface that we are now very familiar with. Specifically in this category, we explored the Roboschool and Gym Retro environments.

We also looked at other useful learning environments that did not necessarily have a Gym-compatible environment interface, but had a very similar API and so it was easy to adapt our agent code or implement a wrapper around the learning environment to make it compatible with the Gym API. Specifically, we explored the famous real-time strategy game-based StarCraft II environment...

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