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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

Product type Paperback

Published in Jul 2018

Publisher Packt

ISBN-13 9781788836579

Length 254 pages

Edition 1st Edition

Languages

Python

Tools

Deep Reinforcement Learning

Concepts

Artificial Intelligence

Author (1):

Palanisamy

View More author details

Table of Contents (12) Chapters

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

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Who this book is for

If you're a student, a game/machine learning developer, or an AI enthusiast looking to get started building intelligent agents and algorithms to solve a variety of problems using learning environments with the OpenAI Gym interface, this book is for you. You will also find this book useful if you want to learn how to build deep reinforcement learning-based, artificially intelligent agents to solve problems in your domain of interest. Though the book covers all the basic concepts that you need to know, some working knowledge of Python will help you get the most out of it.

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Authors (1)

Palanisamy

Palanisamy

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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