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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
Author (1):
Palanisamy P Palanisamy P
Profile icon 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

Summary

In this chapter, we explored the list of Gym environments available on your system, which you installed in the previous chapter, and then understood the naming conventions, or nomenclature, of the environments. We then revisited the agent-environment interaction (the RL loop) diagram and understood how the Gym environment provides the interfaces corresponding to each of the arrows we saw in the image. We then looked at a consolidated summary of the four values returned by the Gym environment's step() method in a tabulated, easy-to-understand format to reinforce your understanding of what they mean!

We also explored in detail the various types of spaces used in the Gym for the observation and action spaces, and we used a script to print out what spaces are used by an environment to understand the Gym environment interfaces better. In our next chapter, we will consolidate...

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