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Hands-On Intelligent Agents with OpenAI Gym

You're reading from   Hands-On Intelligent Agents with OpenAI Gym Your guide to developing AI agents using deep reinforcement learning

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Product type Paperback
Published in Jul 2018
Publisher Packt
ISBN-13 9781788836579
Length 254 pages
Edition 1st Edition
Languages
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Author (1):
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Palanisamy Palanisamy
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Palanisamy
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Table of Contents (12) Chapters Close

Preface 1. Introduction to Intelligent Agents and Learning Environments 2. Reinforcement Learning and Deep Reinforcement Learning FREE CHAPTER 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

Understanding the anatomy of Gym environments

Any Gym-compatible environment should subclass the gym.Env class and implement the reset and step methods and the observation_space and action_space properties and attributes. There is also the opportunity to implement other, optional methods that can add additional functionality to our custom environments. The following table lists and describes the other methods available:

Method
Functionality description
observation_space
The shape and type of the observations returned by the environment.
action_space
The shape and type of the actions accepted by the environment.
reset()
Routines to reset the environment at the start or end of an episode.
step(...)
Routines that calculate the necessary information to advance the environment, simulation, or game to the next step. The routine includes applying the chosen action...
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