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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

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Product type Paperback
Published in Jul 2018
Publisher Packt
ISBN-13 9781788836579
Length 254 pages
Edition 1st Edition
Languages
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Author (1):
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Palanisamy Palanisamy
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Palanisamy
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Table of Contents (12) Chapters Close

Preface 1. Introduction to Intelligent Agents and Learning Environments 2. Reinforcement Learning and Deep Reinforcement Learning FREE CHAPTER 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 went through the step-by-step setup process to install and configure our development environment using conda, OpenAI Gym, and Pytorch! This chapter helped us make sure that we have all the required tools and libraries installed to start developing our agents in Gym environments. In the next chapter, we will explore the features of Gym environments to understand how they work, and how we can use them to train our agents. In Chapter 5, Implementing Your First Learning Agent – Solving the Mountain Car Problem, we will jump right into developing our first reinforcement learning agent to solve the mountain car problem! We will then gradually move on and implement more sophisticated reinforcement learning algorithms in the subsequent chapters.

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