Hands-On Intelligent Agents with OpenAI Gym: Your guide to developing AI agents using deep reinforcement learning

Praveen Palanisamy

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商品描述

Implement intelligent agents using PyTorch to solve classic AI problems, play console games like Atari, and perform tasks such as autonomous driving using the CARLA driving simulator

Key Features

  • Explore the OpenAI Gym toolkit and interface to use over 700 learning tasks
  • Implement agents to solve simple to complex AI problems
  • Study learning environments and discover how to create your own

Book Description

Many real-world problems can be broken down into tasks that require a series of decisions to be made or actions to be taken. The ability to solve such tasks without a machine being programmed requires a machine to be artificially intelligent and capable of learning to adapt. This book is an easy-to-follow guide to implementing learning algorithms for machine software agents in order to solve discrete or continuous sequential decision making and control tasks.

Hands-On Intelligent Agents with OpenAI Gym takes you through the process of building intelligent agent algorithms using deep reinforcement learning starting from the implementation of the building blocks for configuring, training, logging, visualizing, testing, and monitoring the agent. You will walk through the process of building intelligent agents from scratch to perform a variety of tasks. In the closing chapters, the book provides an overview of the latest learning environments and learning algorithms, along with pointers to more resources that will help you take your deep reinforcement learning skills to the next level.

What you will learn

  • Explore intelligent agents and learning environments
  • Understand the basics of RL and deep RL
  • Get started with OpenAI Gym and PyTorch for deep reinforcement learning
  • Discover deep Q learning agents to solve discrete optimal control tasks
  • Create custom learning environments for real-world problems
  • Apply a deep actor-critic agent to drive a car autonomously in CARLA
  • Use the latest learning environments and algorithms to upgrade your intelligent agent development skills

Who this book is 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.

Table of Contents

  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)

商品描述(中文翻譯)

使用PyTorch實現智能代理,解決經典的人工智慧問題,像是玩Atari遊戲,並使用CARLA駕駛模擬器進行自動駕駛等任務。

主要特點:
- 探索OpenAI Gym工具包和介面,使用超過700個學習任務
- 實現代理以解決從簡單到複雜的人工智慧問題
- 研究學習環境,並了解如何創建自己的環境

書籍描述:
許多現實世界的問題可以分解為需要做出一系列決策或採取行動的任務。在不需要對機器進行編程的情況下解決這些任務的能力需要機器具有人工智能並能夠學習適應。本書是一本易於理解的指南,用於實現機器軟體代理的學習演算法,以解決離散或連續的順序決策和控制任務。

《Hands-On Intelligent Agents with OpenAI Gym》將帶領您通過使用深度強化學習來構建智能代理演算法的過程,從配置、訓練、記錄、可視化、測試和監控代理的實現開始。您將逐步構建智能代理以執行各種任務。在最後幾章中,本書提供了最新的學習環境和學習演算法的概述,以及更多資源的指引,幫助您提升深度強化學習技能。

您將學到:
- 探索智能代理和學習環境
- 了解強化學習和深度強化學習的基礎知識
- 使用OpenAI Gym和PyTorch進行深度強化學習的入門
- 發現深度Q學習代理以解決離散最佳控制任務
- 為現實世界問題創建自定義學習環境
- 使用深度演員-評論家代理在CARLA中實現自動駕駛
- 使用最新的學習環境和演算法升級您的智能代理開發技能

本書適合對象:
如果您是學生、遊戲/機器學習開發人員或人工智能愛好者,希望使用OpenAI Gym介面構建智能代理和演算法來解決各種問題,本書適合您。如果您想學習如何構建基於深度強化學習的代理以解決您感興趣的領域中的問題,本書也對您有用。儘管本書涵蓋了您需要了解的所有基本概念,但對Python編程語言的一些工作知識將幫助您充分利用本書。

目錄:
1. 智能代理和學習環境簡介
2. 強化學習和深度強化學習
3. 開始使用OpenAI Gym和深度強化學習
4. 探索Gym及其功能
5. 實現第一個學習代理-解決Mountain Car問題
6. 使用深度Q學習實現最佳控制的智能代理
7. 創建自定義OpenAI Gym環境-Carla駕駛模擬器
8. 使用深度演員-評論家演算法實現智能和自主駕駛代理
9. 探索學習環境景觀-Roboschool、Gym-Retro、StarCraft-II、DeepMindLab
10. 探索學習演算法景觀-DDPG(演員-評論家)、PPO(策略梯度)、Rainbow(基於值的)