24-28 June 2019
Europe/Zurich timezone
There is a live webcast for this event.

Deep Reinforcement Learning and the Type IIA Landscape

27 Jun 2019, 11:30
500/1-001 - Main Auditorium (CERN)

500/1-001 - Main Auditorium


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Brent Nelson (Northeastern University)


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.

Presentation Materials