# Coordination-driven learning in multi-agent problem spaces

arXiv: https://arxiv.org/abs/1809.04918 · HTML: https://mlsanity.com/paper/1809.04918/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2018.

## Built on by (most cited first, 4 of 4)

- [On games and simulators as a platform for development of artificial intelligence for command and control](https://mlsanity.com/paper/2110.11305/) (2021)
- [Influence-Based Reinforcement Learning for Intrinsically-Motivated Agents](https://mlsanity.com/paper/2108.12581/) (2021)
- [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)

## Abstract (arXiv)

We discuss the role of coordination as a direct learning objective in multi-agent reinforcement learning (MARL) domains. To this end, we present a novel means of quantifying coordination in multi-agent systems, and discuss the implications of using such a measure to optimize coordinated agent policies. This concept has important implications for adversary-aware RL, which we take to be a sub-domain of multi-agent learning.