Deep Reinforcement Learning in Unity: With Unity ML Toolkit

Majumder, Abhilash

  • 出版商: Apress
  • 出版日期: 2020-12-27
  • 定價: $2,450
  • 售價: 9.5$2,328
  • 貴賓價: 9.0$2,205
  • 語言: 英文
  • 頁數: 564
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1484265025
  • ISBN-13: 9781484265024
  • 相關分類: Reinforcement遊戲引擎 Game-engineDeepLearning
  • 立即出貨 (庫存=1)

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商品描述

Gain an in-depth overview of reinforcement learning for autonomous agents in game development with Unity.

This book starts with an introduction to state-based reinforcement learning algorithms involving Markov models, Bellman equations, and writing custom C# code with the aim of contrasting value and policy-based functions in reinforcement learning. Then, you will move on to path finding and navigation meshes in Unity, setting up the ML Agents Toolkit (including how to install and set up ML agents from the GitHub repository), and installing fundamental machine learning libraries and frameworks (such as Tensorflow). You will learn about: deep learning and work through an introduction to Tensorflow for writing neural networks (including perceptron, convolution, and LSTM networks), Q learning with Unity ML agents, and porting trained neural network models in Unity through the Python-C# API. You will also explore the OpenAI Gym Environment used throughout the book.

Deep Reinforcement Learning in Unity provides a walk-through of the core fundamentals of deep reinforcement learning algorithms, especially variants of the value estimation, advantage, and policy gradient algorithms (including the differences between on and off policy algorithms in reinforcement learning). These core algorithms include actor critic, proximal policy, and deep deterministic policy gradients and its variants. And you will be able to write custom neural networks using the Tensorflow and Keras frameworks.

Deep learning in games makes the agents learn how they can perform better and collect their rewards in adverse environments without user interference. The book provides a thorough overview of integrating ML Agents with Unity for deep reinforcement learning.

 

What You Will Learn

 

  • Understand how deep reinforcement learning works in games
  • Grasp the fundamentals of deep reinforcement learning
  • Integrate these fundamentals with the Unity ML Toolkit SDK
  • Gain insights into practical neural networks for training Agent Brain in the context of Unity ML Agents
  • Create different models and perform hyper-parameter tuning
  • Understand the Brain-Academy architecture in Unity ML Agents
  • Understand the Python-C# API interface during real-time training of neural networks
  • Grasp the fundamentals of generic neural networks and their variants using Tensorflow
  • Create simulations and visualize agents playing games in Unity

 

 

 

 

 

 

Who This Book Is For

Readers with preliminary programming and game development experience in Unity, and those with experience in Python and a general idea of machine learning

商品描述(中文翻譯)

在Unity遊戲開發中,深度強化學習是自主代理的一個重要領域。本書提供了對於使用Unity進行深度強化學習的詳細概述。

本書首先介紹了基於狀態的強化學習算法,包括馬爾可夫模型、貝爾曼方程和使用自定義C#代碼來對比強化學習中的價值和策略函數。接著,您將學習在Unity中進行路徑尋找和導航網格,設置ML Agents Toolkit(包括如何從GitHub存儲庫安裝和設置ML Agents),以及安裝基本的機器學習庫和框架(如Tensorflow)。您將學習深度學習,並通過Tensorflow介紹神經網絡的寫作(包括感知器、卷積和LSTM網絡),使用Unity ML Agents進行Q學習,以及通過Python-C# API在Unity中移植訓練好的神經網絡模型。您還將探索本書中使用的OpenAI Gym環境。

《Unity中的深度強化學習》提供了深度強化學習算法的核心基礎知識,特別是價值估計、優勢和策略梯度算法的變體(包括強化學習中的策略算法和離線算法之間的區別)。這些核心算法包括演員評論家、近端策略和深度確定性策略梯度及其變體。您將能夠使用Tensorflow和Keras框架編寫自定義神經網絡。

在遊戲中進行深度學習可以使代理學習如何在不需要用戶干預的情況下在惡劣環境中表現更好並獲得獎勵。本書全面介紹了如何將ML Agents與Unity集成以進行深度強化學習。

本書的學習內容包括:

- 理解深度強化學習在遊戲中的應用
- 掌握深度強化學習的基礎知識
- 將這些基礎知識與Unity ML Toolkit SDK集成
- 在Unity ML Agents的上下文中獲得關於訓練代理大腦的實用神經網絡的見解
- 創建不同的模型並進行超參數調整
- 理解Unity ML Agents中的Brain-Academy架構
- 在實時訓練神經網絡時理解Python-C# API接口
- 掌握使用Tensorflow創建通用神經網絡及其變體的基礎知識
- 在Unity中創建模擬並可視化代理玩遊戲的情景

本書適合具有Unity初步編程和遊戲開發經驗,以及具有Python和機器學習基礎知識的讀者。

作者簡介

Abhilash Majumder is a natural language processing research engineer for HSBC (UK/India) and technical mentor for Udactiy (ML). He also has been associated with Unity Technologies and was a speaker at Unite India-18, and has educated close to 1,000 students from EMEA and SEPAC (India) on Unity. He is an ML contributor and curator for Open Source Google Research and Tensorflow, and creator of ML libraries under Python Package Index (Pypi). He is an online educationalist for Udemy and a deep learning mentor for Upgrad.

Abhilash was an apprentice/student ambassador for Unity Technologies where he educated corporate employees and students on using general Unity for game development. He was a technical mentor (AI programming) for the Unity Ambassadors Community and Content Production. He has been associated with Unity Technologies for general education, with an emphasis on graphics and machine learning. He is one of the first content creators for Unity Technologies India since 2017.

 

作者簡介(中文翻譯)

Abhilash Majumder 是 HSBC(英國/印度)的自然語言處理研究工程師,也是 Udacity(機器學習)的技術導師。他還曾與 Unity Technologies 合作,並在 Unite India-18 上發表演講,教授了來自 EMEA 和 SEPAC(印度)的近 1,000 名 Unity 學生。他是 Google Research 和 Tensorflow 的機器學習貢獻者和策展人,並在 Python Package Index(Pypi)下創建了機器學習庫。他是 Udemy 的線上教育專家,也是 Upgrad 的深度學習導師。

Abhilash 曾是 Unity Technologies 的學徒/學生大使,他在這裡教導企業員工和學生如何使用 Unity 進行遊戲開發。他還是 Unity 大使社區和內容製作的技術導師(AI 編程)。他與 Unity Technologies 有著長期的合作關係,專注於圖形和機器學習的教育。自 2017 年以來,他是 Unity Technologies India 的首批內容創作者之一。