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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
Author Profile Icon Palanisamy
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

What is an intelligent agent?

A major goal of artificial intelligence is to build intelligent agents. Perceiving their environment, understanding, reasoning and learning to plan, and making decisions and acting upon them are essential characteristics of intelligent agents. We will begin our first chapter by understanding what an intelligent agent is, from the basic definition of agents, to adding intelligence on top of that.

An agent is an entity that acts based on the observation (perception) of its environment. Humans and robots are examples of agents with physical forms.

A human, or an animal, is an example of an agent that uses its organs (eyes, ears, nose, skin, and so on) as sensors to observe/perceive its environment and act using their physical body (arms, hands, legs, head, and so on). A robot uses its sensors (cameras, microphones, LiDAR, radar, and so on) to observe/perceive its environment and act using its physical robotic body (robotic arms, robotic hands/grippers, robotic legs, speakers, and so on).

Software agents are computer programs that are capable of making decisions and taking actions through interaction with their environment. A software agent can be embodied in a physical form, such as a robot. Autonomous agents are entities that make decisions autonomously and take actions based on their understanding of and reasoning about their observations of their environment.

An intelligent agent is an autonomous entity that can learn and improve based on its interactions with its environment. An intelligent agent is capable of analyzing its own behavior and performance using its observations.

In this book, we will develop intelligent agents to solve sequential decision-making problems that can be solved using a sequence of (independent) decisions/actions in a (loosely) Markovian environment, where feedback in the form of reward signals is available (through percepts), at least in some environmental conditions.

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