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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 |
|---|---|
| Autor Corporativo: | |
| Formato: | Electrónico Congresos, conferencias Video |
| Idioma: | Inglés |
| Publicado: |
[Place of publication not identified] :
O'Reilly,
2019.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 037 | |a CL0501000081 |b Safari Books Online | ||
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| 049 | |a UAMI | ||
| 100 | 1 | |a Lange, Danny B., |e on-screen presenter. | |
| 245 | 1 | 0 | |a Learning from multiagent emergent behaviors in a simulated environment / |c Danny Lange. |
| 264 | 1 | |a [Place of publication not identified] : |b O'Reilly, |c 2019. | |
| 300 | |a 1 online resource (1 streaming video file (44 min., 15 sec.)) | ||
| 336 | |a two-dimensional moving image |b tdi |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 337 | |a video |b v |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 511 | 0 | |a Presenter, Danny Lange. | |
| 500 | |a Title from title screen (viewed November 14, 2019). | ||
| 518 | |a Recorded at the 2019 O'Reilly Artificial Intelligence Conference in New York. | ||
| 520 | |a "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 | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 2 | |a Artificial Intelligence | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Intelligence artificielle. | |
| 650 | 7 | |a artificial intelligence. |2 aat | |
| 650 | 7 | |a Artificial intelligence. |2 fast |0 (OCoLC)fst00817247 | |
| 650 | 7 | |a Machine learning. |2 fast |0 (OCoLC)fst01004795 | |
| 711 | 2 | |a O'Reilly Artificial Intelligence Conference |d (2019 : |c New York, N.Y.) |j issuing body. | |
| 856 | 4 | 0 | |u https://learning.oreilly.com/videos/~/0636920339427/?ar |z Texto completo (Requiere registro previo con correo institucional) |
| 994 | |a 92 |b IZTAP | ||