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Measuring Collaborative Emergent Behavior in Multi-agent Reinforcement Learning

  • Sean L. BartonEmail author
  • Nicholas R. Waytowich
  • Erin Zaroukian
  • Derrik E. Asher
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 876)

Abstract

Multi-agent reinforcement learning (RL) has important implications for the future of human-agent teaming. We show that improved performance with multi-agent RL is not a guarantee of the collaborative behavior thought to be important for solving multi-agent tasks. To address this, we present a novel approach for quantitatively assessing collaboration in continuous spatial tasks with multi-agent RL. Such a metric is useful for measuring collaboration between computational agents and may serve as a training signal for collaboration in future RL paradigms involving humans.

Keywords

Multi-agent reinforcement learning Deep reinforcement learning Human-agent teaming Collaboration 

Notes

Acknowledgements and Disclosure

This research was sponsored by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-18-2-0058. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.

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Copyright information

© This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2019

Authors and Affiliations

  • Sean L. Barton
    • 1
    Email author
  • Nicholas R. Waytowich
    • 2
  • Erin Zaroukian
    • 1
  • Derrik E. Asher
    • 1
  1. 1.Computational and Information Sciences DirectorateU.S. Army Research LaboratoryAdelphiUSA
  2. 2.Human Research and Engineering DirectorateU.S. Army Research LaboratoryAdelphiUSA

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