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.
Table of contents
- Title: Learning from multiagent emergent behaviors in a simulated environment
- Release date: October 2019
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 0636920339403
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