Optimizing agent behavior over long time scales by transporting value

People are able to mentally time travel to distant memories and reflect on the consequences of those past events. Here, the authors show how a mechanism that connects learning from delayed rewards with memory retrieval can enable AI agents to discover links between past events to help decide better...

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Autores principales: Chia-Chun Hung, Timothy Lillicrap, Josh Abramson, Yan Wu, Mehdi Mirza, Federico Carnevale, Arun Ahuja, Greg Wayne
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2019
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Acceso en línea:https://doaj.org/article/2e805e7216b343f9a8b03a4f5ce02aad
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