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Optimizing agent behavior over long time scales by transporting value

Author

Listed:
  • Chia-Chun Hung

    (DeepMind)

  • Timothy Lillicrap

    (DeepMind)

  • Josh Abramson

    (DeepMind)

  • Yan Wu

    (DeepMind)

  • Mehdi Mirza

    (DeepMind)

  • Federico Carnevale

    (DeepMind)

  • Arun Ahuja

    (DeepMind)

  • Greg Wayne

    (DeepMind)

Abstract

Humans prolifically engage in mental time travel. We dwell on past actions and experience satisfaction or regret. More than storytelling, these recollections change how we act in the future and endow us with a computationally important ability to link actions and consequences across spans of time, which helps address the problem of long-term credit assignment: the question of how to evaluate the utility of actions within a long-duration behavioral sequence. Existing approaches to credit assignment in AI cannot solve tasks with long delays between actions and consequences. Here, we introduce a paradigm where agents use recall of specific memories to credit past actions, allowing them to solve problems that are intractable for existing algorithms. This paradigm broadens the scope of problems that can be investigated in AI and offers a mechanistic account of behaviors that may inspire models in neuroscience, psychology, and behavioral economics.

Suggested Citation

  • Chia-Chun Hung & Timothy Lillicrap & Josh Abramson & Yan Wu & Mehdi Mirza & Federico Carnevale & Arun Ahuja & Greg Wayne, 2019. "Optimizing agent behavior over long time scales by transporting value," Nature Communications, Nature, vol. 10(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-13073-w
    DOI: 10.1038/s41467-019-13073-w
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