Language:
English
Pages:
1 online resource (1 video file, approximately 44 min.)
Edition:
1st edition
Keywords:
Electronic videos ; local
Abstract:
Traditionally, determining the most efficient designs and practices—whether for determining how store merchandise should be arranged or where people and machines should be laid out in a factory floor—has required vast amounts of data and human assessment. These efficient designs can be the difference between a thriving company and a struggling one. Recent advancements in multiagent reinforcement learning within virtual environments, such as DeepMind’s Capture the Flag or Open AI’s Learning to Compete and Cooperate, have led to a novel approach for tackling efficient design and practices. Danny Lange (Unity Technologies) explains how observing emergent behaviors of multiple AI agents in a simulated virtual environment can lead to the most optimal designs and real-world practices, all without introducing human bias or the need for vast amounts of data. This session was recorded at the 2019 O'Reilly Artificial Intelligence Conference in New York.
Note:
Online resource; Title from title screen (viewed October 31, 2019)
