Coordination-driven learning in multi-agent problem spaces
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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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Sep 2018.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 0; 2024: 0; 2025: 1.
4 papers.
- 2021On games and simulators as a platform for development of artificial intelligence for command and controlcited by 3
- 2021Influence-Based Reinforcement Learning for Intrinsically-Motivated Agentscited by 2
- 2025Who is Helping Whom? Analyzing Inter-dependencies to Evaluate Cooperation in Human-AI Teamingcited by 2
- 2022Strategic Maneuver and Disruption with Reinforcement Learning Approaches for Multi-Agent Coordinationcited by 1
- 2025Who is Helping Whom? Analyzing Inter-dependencies to Evaluate Cooperation in Human-AI Teaming10 Feb 2025
- 2022Strategic Maneuver and Disruption with Reinforcement Learning Approaches for Multi-Agent Coordination17 Mar 2022
- 2021On games and simulators as a platform for development of artificial intelligence for command and control21 Oct 2021
- 2021Influence-Based Reinforcement Learning for Intrinsically-Motivated Agents28 Aug 2021