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Learning from multiagent emergent behaviors in a simulated environment /
"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 d...
| Clasificación: | Libro Electrónico |
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| Autor Corporativo: | |
| Formato: | Electrónico Congresos, conferencias Video |
| Idioma: | Inglés |
| Publicado: |
[Place of publication not identified] :
O'Reilly,
2019.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
| Sumario: | "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."--Resource description page |
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| Notas: | Title from title screen (viewed November 14, 2019). |
| Descripción Física: | 1 online resource (1 streaming video file (44 min., 15 sec.)) |