Learn Unity ML-Agents – Fundamentals of Unity Machine Learning: Incorporate new powerful ML algorithms such as Deep Reinforcement Learning for games

Transform games into environments using machine learning and Deep learning with Tensorflow, Keras, and Unity

Key Features

• Learn how to apply core machine learning concepts to your games with Unity
• Learn the Fundamentals of Reinforcement Learning and Q-Learning and apply them to your games
• Learn How to build multiple asynchronous agents and run them in a training scenario

Book Description

Unity Machine Learning agents allow researchers and developers to create games and simulations using the Unity Editor, which serves as an environment where intelligent agents can be trained with machine learning methods through a simple-to-use Python API.

This book takes you from the basics of Reinforcement and Q Learning to building Deep Recurrent Q-Network agents that cooperate or compete in a multi-agent ecosystem. You will start with the basics of Reinforcement Learning and how to apply it to problems. Then you will learn how to build self-learning advanced neural networks with Python and Keras/TensorFlow. From there you move o n to more advanced training scenarios where you will learn further innovative ways to train your network with A3C, imitation, and curriculum learning models. By the end of the book, you will have learned how to build more complex environments by building a cooperative and competitive multi-agent ecosystem.

What you will learn

• Develop Reinforcement and Deep Reinforcement Learning for games.
• Understand complex and advanced concepts of reinforcement learning and neural networks
• Explore various training strategies for cooperative and competitive agent development
• Adapt the basic script components of Academy, Agent, and Brain to be used with Q Learning.
• Enhance the Q Learning model with improved training strategies such as Greedy-Epsilon exploration
• Implement a simple NN with Keras and use it as an external brain in Unity
• Understand how to add LTSM blocks to an existing DQN
• Build multiple asynchronous agents and run them in a training scenario

Who This Book Is For

This book is intended for developers with an interest in using Machine learning algorithms to develop better games and simulations with Unity.

Table of Contents

1. Introducing Machine Learning & ML-Agents
2. The Bandit and Reinforcement Learning
3. Deep Reinforcement Learning with Python
4. Adding Agent Exploration and Memory
5. Playing the Game
6. Terrarium Revisited – Building A Multi-Agent Ecosystem

將以上文字翻譯成繁體中文如下：

將遊戲轉化為環境：使用Tensorflow、Keras和Unity進行機器學習和深度學習

主要特點：
- 學習如何在Unity中應用核心機器學習概念到遊戲中
- 學習強化學習和Q學習的基礎，並將其應用到遊戲中
- 學習如何構建多個異步代理並在訓練場景中運行它們

書籍描述：
Unity機器學習代理使研究人員和開發人員能夠使用Unity編輯器創建遊戲和模擬，該編輯器作為一個環境，智能代理可以通過簡單易用的Python API進行機器學習方法的訓練。

本書從強化學習和Q學習的基礎知識開始，逐步介紹如何構建深度循環Q網絡代理，這些代理可以在多代理生態系統中合作或競爭。您將從強化學習的基礎知識入手，並學習如何將其應用於問題解決。然後，您將學習如何使用Python和Keras/TensorFlow構建自學習的高級神經網絡。從那裡，您將進一步學習更高級的訓練場景，並探索使用A3C、模仿和課程學習模型進行網絡訓練的創新方法。通過閱讀本書，您將學會如何通過構建合作和競爭的多代理生態系統來構建更複雜的環境。

您將學到：
- 開發遊戲的強化學習和深度強化學習
- 理解強化學習和神經網絡的複雜和高級概念
- 探索合作和競爭代理開發的各種訓練策略
- 將Academy、Agent和Brain的基本腳本組件適應於Q學習
- 通過改進的訓練策略（如貪婪-ε探索）增強Q學習模型
- 使用Keras實現簡單的神經網絡，並將其作為Unity中的外部大腦
- 理解如何將LTSM塊添加到現有的DQN中
- 構建多個異步代理並在訓練場景中運行它們

本書適合對使用機器學習算法開發更好的遊戲和模擬有興趣的開發人員。

目錄：
1. 介紹機器學習和ML-Agents
2. 搶劫者和強化學習
3. 使用Python進行深度強化學習
4. 添加代理探索和記憶
5. 玩遊戲
6. Terrarium再訪 - 構建多代理生態系統