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Talk32 min

Deep Reinforcement Learning and the Type IIA Landscape

Brent Nelson · CERN

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About

An artificial intelligence agent known as an asynchronous advantage actor-critic is utilized to explore type IIA compactifications with intersecting D6-branes. By reinforcement learning, the agent's performance in satisfying string consistency conditions, and finding Standard Model like configurations, is significantly improved. In one case, we demonstrate that the agent learns a human-derived strategy for finding consistent string models. In another case, where no human-derived strategy exists, the agent learns a genuinely new strategy that achieves the same goal twice as efficiently per unit time..

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The talk

  1. Deep Reinforcement Learning and the Type IIA Landscape 2019-06-27 · 32 min · CERN's player

Credits and licence

Institution
CERN
Department
Conferences & Workshops
Speaker
Brent Nelson
Year
2019
Language
English
Source
CERN
Licence
CERN, as CERN states it. CERN's terms
Attribution
© CERN. CERN provides its audiovisual media free of charge for educational and informational use, credited to CERN; this lecture is shown in CERN's own player.
Who can watch
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