| 书目名称 | Deep Reinforcement Learning in Unity | | 副标题 | With Unity ML Toolki | | 编辑 | Abhilash Majumder | | 视频video | | | 概述 | Contains a descriptive view of the core reinforcement learning algorithms involving Unity ML Agents and how they can be leveraged in games to AI create agents.Covers autonomous driving AI with modeled | | 图书封面 |  | | 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 (inclu | | 出版日期 | Book 2021 | | 关键词 | Deep Learning; Reinforcement Learning; Tensorflow; Keras; Unity; ML Toolkit; Neural Network; Autonomous Ag | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4842-6503-1 | | isbn_softcover | 978-1-4842-6502-4 | | isbn_ebook | 978-1-4842-6503-1 | | copyright | Abhilash Majumder 2021 |
The information of publication is updating
|
|
发表于 2025-3-21 17:49:46
