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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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Training the deep Q-learner to play Atari games

We have gone through several new techniques in this chapter. You deserve a pat on your back for making it this far! Now starts the fun part where you can let your agents train by themselves to play several Atari games and see how they are progressing. What is great about our deep Q-learner is the fact that we can use the same agent to train and play any of the Atari games!

By the end of this section, you should be able to use our deep Q learning agent to observe the pixels on the screen and take actions by sending the joystick commands to the Atari Gym environment, just like what is shown in the following screenshot:

Putting together a comprehensive deep Q-learner

It is time...

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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.

See other products by Palanisamy P

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