Hands-On Intelligent Agents with OpenAI Gym
Walks through the hands-on process of building intelligent agents from the basics and all the way up to solving complex problems including playing Atari games and driving a car autonomously in the CARLA simulator. Discusses various learning environments and how to transform real-world problems into learning environments and solve using the agents.
Offered by
Difficulty Level
Intermediate
Completion Time
8h28m
Language
English
About Book
Who Is This Book For?
If you’re a student, game/machine learning developer, or AI enthusiast looking to get started with building intelligent agents and algorithms to solve a variety of problems with the OpenAI Gym interface, this book is for you. You will also find this book useful if you want to learn how to build deep reinforcement learning-based agents to solve problems in your domain of interest. Though the book covers all the basic concepts that you need to know, some working knowledge of Python programming language will help you get the most out of it.
Hands-On Intelligent Agents with OpenAI Gym
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 8h28m total length
Introduction to Intelligent Agents and Learning Environments
Reinforcement Learning and Deep Reinforcement Learning
Getting Started with OpenAI Gym and Deep Reinforcement Learning
Exploring the Gym and its Features
Implementing your First Learning Agent – Solving the Mountain Car problem
Implementing an Intelligent Agent for Optimal Control using Deep Q-Learning
Creating Custom OpenAI Gym Environments – Carla Driving Simulator
Implementing an Intelligent & Autonomous Car Driving Agent using Deep Actor-Critic Algorithm
Exploring the Learning Environment Landscape – Roboschool, Gym-Retro, StarCraft-II, DeepMindLab
Exploring the Learning Algorithm Landscape – DDPG (Actor-Critic), PPO (Policy-Gradient), Rainbow (Value-Based)
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