Danny Lange demonstrates the role games can play in driving the development of reinforcement learning algorithms. Danny uses the Unity Engine with the ML-Agents toolkit as an example of how dynamic 3D game environments can be utilized for machine learning research. Along the way, Danny offers an overview of the kinds of algorithms being used to train machine learning agents, including various reinforcement learning and supervised learning methods. You’ll walk away ready to conduct machine learning research within custom-made 3D game environments, using a deep reinforcement learning methodology that can be applied to solving a variety of tasks in these environments.
Danny Lange将演示游戏在推动强化学习算法的发展过程中所扮演的角色。 Danny使用ML-Agents工具包的Unity Engine作为动态3D游戏环境用于机器学习研究的例子。在此过程中，Danny还会概述用于训练机器学习代理的各种算法，包括各种强化学习和监督式学习方法。结束时，你将能了解如何在定制的3D游戏环境中进行机器学习研究，使用深度强化学习方法去解决这些环境中的各种任务。
Danny Lange is vice president of AI and machine learning at Unity Technologies, where he leads multiple initiatives around applied artificial intelligence. Previously, Danny was head of machine learning at Uber, where he led the efforts to build a highly scalable machine learning platform to support all parts of Uber’s business, from the Uber app to self-driving cars; general manager of Amazon Machine Learning, where he provided internal teams with access to machine intelligence and launched an AWS product that offers machine learning as a cloud service to the public; principal development manager at Microsoft, where he led a product team focused on large-scale machine learning for big data; CTO of General Magic, Inc.; and founder of his own company, Vocomo Software, where he worked on General Motor’s OnStar Virtual Advisor, one of the largest deployments of an intelligent personal assistant until Siri. Danny started his career as a computer scientist at IBM Research. He is a member of ACM and IEEE Computer Society and has numerous patents to his credit. Danny holds an MS and PhD in computer science from the Technical University of Denmark.
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