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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

Exploring the Learning Environment Landscape - Roboschool, Gym-Retro, StarCraft-II, DeepMindLab

You have come a long way in your quest to get hands-on experience in building intelligent agents to solve a variety of challenging problems. In the previous chapters, we looked into several environments that are available in OpenAI Gym. In this chapter, we will look beyond the Gym and look at some of the other well developed environments that you can use to train your intelligent agents or run experiments.

Before we look at other open source libraries that provide good learning environments for developing intelligent agents, let's have a look at a recent class of environments added to the OpenAI Gym library. If, like me, you are interested in robotics, you will like this one a lot. Yes! It is the robotics class of environments, which provides very useful environments for robotic...

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