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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 list of environments and nomenclature

Let's start by picking an environment and understanding the Gym interface. You may already be familiar with the basic function calls to create a Gym environment from the previous chapters, where we used them to test our installations. Here, we will formally go through them.

Let's activate the rl_gym_book conda environment and open a Python prompt. The first step is to import the Gym Python module using the following line of code:

import gym

We can now use the gym.make method to create an environment from the available list of environments. You may be asking how to find the list of Gym environments available on your system. We will create a small utility script to generate the list of environments so that you can refer to it later when you need to. Let's create a script named list_gym_envs.py under the ~/rl_gym_book...

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