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Advancing our understanding of deep reinforcement learning with community-driven insights /
"Simulated environments have been essential to advancing the field of artificial intelligence, providing vast amounts of synthetic data that tests novel approaches safely and efficiently. This has most often taken the form of games, ranging from simple board games to modern multiplayer strategy...
| Clasificación: | Libro Electrónico |
|---|---|
| Formato: | Electrónico Video |
| Idioma: | Inglés |
| Publicado: |
[Place of publication not identified] :
O'Reilly Media,
2019.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Lange, Danny B., |e on-screen presenter. | |
| 245 | 1 | 0 | |a Advancing our understanding of deep reinforcement learning with community-driven insights / |c Danny Lange. |
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| 511 | 0 | |a Presenter, Danny Lange. | |
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| 520 | |a "Simulated environments have been essential to advancing the field of artificial intelligence, providing vast amounts of synthetic data that tests novel approaches safely and efficiently. This has most often taken the form of games, ranging from simple board games to modern multiplayer strategy games. These games served as a good starting point, but Danny Lange (Unity Technologies) reveals an opportunity to push the state of the art in AI research to the next level. United introduced the Obstacle Tower, a high-visual-fidelity, 3-D, third-person, procedurally generated game environment purpose built to test a deep reinforcement learning-trained agent's vision, control, planning, and generalization abilities. Over the past year, Unity invited researchers and developers to try to solve the tower with the intention of sharing those insights with the broader community. This session is from the 2019 O'Reilly Artificial Intelligence Conference in San Jose, CA."--Resource description page | ||
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| 650 | 0 | |a Reinforcement learning. | |
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| 650 | 6 | |a Apprentissage par renforcement (Intelligence artificielle) | |
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