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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

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

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Exploring the Gym and its Features

Now that you have a working setup, we will start exploring the various features and options provided by the Gym toolkit. This chapter will walk you through some of the commonly used environments, the tasks they solve, and what it would take for your agent to master a task.

In this chapter, we will explore the following topics:

Exploring the various types of Gym environment
Understanding the structure of the reinforcement learning loop
Understanding the different observation and action spaces

The rest of the chapter is locked

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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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