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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

Concepts

Artificial Intelligence

Author (1):

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

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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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Authors (1)

Palanisamy P

Praveen Palanisamy works on developing autonomous intelligent systems. He is currently an AI researcher at General Motors R&D. He develops planning and decision-making algorithms and systems that use deep reinforcement learning for autonomous driving. Previously, he was at the Robotics Institute, Carnegie Mellon University, where he worked on autonomous navigation, including perception and AI for mobile robots. He has experience developing complete, autonomous, robotic systems from scratch.

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