# Measuring collaborative emergent behavior in multi-agent reinforcement learning

arXiv: https://arxiv.org/abs/1807.08663 · HTML: https://mlsanity.com/paper/1807.08663/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2018.

## Built on by (most cited first, 5 of 5)

- [A Survey on Causal Reinforcement Learning](https://mlsanity.com/paper/2302.05209/) (2023)
- [Measuring Value Alignment](https://mlsanity.com/paper/2312.15241/) (2023)
- [Who is Helping Whom? Analyzing Inter-dependencies to Evaluate Cooperation in Human-AI Teaming](https://mlsanity.com/paper/2502.06976/) (2025)
- [Strategic Maneuver and Disruption with Reinforcement Learning Approaches for Multi-Agent Coordination](https://mlsanity.com/paper/2203.09565/) (2022)
- [Reliably Re-Acting to Partner's Actions with the Social Intrinsic Motivation of Transfer Empowerment](https://mlsanity.com/paper/2203.03355/) (2022)

## Abstract (arXiv)

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.