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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
Toc

Table of Contents (12) Chapters close

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

Spaces in the Gym

We can see that each environment in the Gym is different. Every game environment under the Atari category is also different from the others. For example, in the case of the VideoPinball-v0 environment, the goal is to keep bouncing a ball with two paddles to collect points based on where the ball hits, and to make sure that the ball never falls below the paddles, whereas in the case of Alien-v0, which is another Atari game environment, the goal is to move through a maze (the rooms in a ship) collecting dots, which are equivalent to destroying the eggs of the alien. Aliens can be killed by collecting a pulsar dot and the reward/score increases when that happens. Do you see the variations in the games/environments? How do we know what types of actions are valid in a game?

In the VideoPinball environment, naturally, the actions are to move the paddles up or down...

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