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Get Started With Unity* Machine Learning Using Intel® Distribution for Python* (Part 1)

This article will show game developers how to use reinforcement learning to create better artificial intelligence (AI) behavior. Using Intel® Distribution for Python—an improved version of the popular object-oriented, high-level programming language—readers will glean how to train pre-existing machine-language (ML) agents to learn and adapt. In this scenario, we will use Intel® Optimization for...
Authored by Manisha B. Last updated on 12/09/2019 - 12:20
Article

Game Dev with Unity* ML-Agents and Intel® Optimized Python* (Part Two)

In the final part of this two-part series on machine learning with Unity* ML-Agents, we will dig deeper into the architecture and create an ML-Agent from scratch. Before training, we will inspect the files that require parameters for machine learning to proceed. Finally, we will train the agent using Intel® optimized Python* and show how the completed system works.
Authored by Manisha B. Last updated on 12/09/2019 - 12:20
Article

Unity* ML-Agent 和英特尔® 优化 Python* 助力游戏开发(第二部分)

借助 Unity* ML-Agent 进行机器学习系列总共包含两个部分,这是其中的最后一部分,我们将深入研究其体系结构并从头开始创建 ML-Agent。训练之前,我们将检查需要参数以便机器学习继续进行的文件。最后,我们将使用英特尔®优化 Python* 训练代理,并展示最终的系统如何运行。
Authored by Manisha B. Last updated on 12/09/2019 - 12:20
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使用 Python* 的英特尔® 分发版实现 Unity* 机器学习入门(第 1 部分)

本文将向游戏开发人员介绍如何使用强化学习创建更好的人工智能 (AI) 行为。使用 Python* 的英特尔® 分发版 — 常用面向对象的高级编程语言的进阶版 — 读者可收集关于如何训练预先存在的机器语言 (ML) 代理学习和适应的信息。在此场景下,我们将使用英特尔® Optimization for TensorFlow* 在本地化环境中运行 Unity * ML-Agents。
Authored by Manisha B. Last updated on 12/09/2019 - 12:20