Measuring collaborative emergent behavior in multi-agent reinforcement learning

Sean L. Barton; Nicholas R. Waytowich; Erin Zaroukian; Derrik E. Asher

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arXiv · 1807.08663

Measuring collaborative emergent behavior in multi-agent reinforcement learning

Sean L. Barton·Nicholas R. Waytowich·Erin Zaroukian·Derrik E. Asher

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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.

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Sean L. Barton, Nicholas R. Waytowich, Erin Zaroukian, Derrik E. Asher. 2018-07-23. Measuring collaborative emergent behavior in multi-agent reinforcement learning. https://arxiv.org/abs/1807.08663

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