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arxiv_ai 95% Match Research Paper Multi-agent systems researchers,Reinforcement learning researchers,Robotics engineers,Game AI developers 1 week ago

Multi-party Agent Relation Sampling for Multi-party Ad Hoc Teamwork

reinforcement-learning › multi-agent
📄 Abstract

Abstract: Multi-agent reinforcement learning (MARl) has achieved strong results in cooperative tasks but typically assumes fixed, fully controlled teams. Ad hoc teamwork (AHT) relaxes this by allowing collaboration with unknown partners, yet existing variants still presume shared conventions. We introduce Multil-party Ad Hoc Teamwork (MAHT), where controlled agents must coordinate with multiple mutually unfamiliar groups of uncontrolled teammates. To address this, we propose MARs, which builds a sparse skeleton graph and applies relational modeling to capture cross-group dvnamics. Experiments on MPE and starCralt ll show that MARs outperforms MARL and AHT baselines while converging faster.
Authors (3)
Beiwen Zhang
Yongheng Liang
Hejun Wu
Submitted
October 29, 2025
arXiv Category
cs.MA
arXiv PDF

Key Contributions

Introduces Multi-party Ad Hoc Teamwork (MAHT), a challenging setting where agents coordinate with multiple unfamiliar groups of uncontrolled teammates. Proposes MARs, a method using relational modeling on sparse skeleton graphs to capture cross-group dynamics, outperforming MARL and AHT baselines.

Business Value

Enables more flexible and adaptable multi-agent systems, crucial for scenarios like swarm robotics, collaborative logistics, and complex team-based games where agents must dynamically form and adapt to teams.

Paper Metadata

Innovation Type

Problem Formulation and Algorithmic Innovation

Deployment Feasibility

Moderate, requires sophisticated multi-agent simulation environments for training and testing.

Limitations Addressed

Existing MARL assumes fixed teams.,Existing AHT assumes shared conventions and often single unknown partners.,Difficulty in coordinating with multiple, mutually unfamiliar groups of agents.

Performance Gains

Outperforms MARL and AHT baselines,Converges faster

Technical Tags

Multi-party Ad Hoc Teamwork (MAHT)Multi-Agent Reinforcement Learning (MARL)Ad Hoc Teamwork (AHT)Relational ModelingSparse Skeleton GraphCross-group DynamicsCoordinationUnknown PartnersUncontrolled TeammatesMARs

Research Topics

Multi-Agent SystemsReinforcement LearningGame TheoryCoordination

Methods & Architectures

Multi-party Ad Hoc Teamwork (MAHT) frameworkMARs algorithmSparse skeleton graph constructionRelational modeling MARs

Applications & Tasks

Robotics Game AI Simulation Environments Multi-agent CoordinationAd Hoc TeamworkLearning with Unknown Agents Coordinating with multiple unknown groups of agentsAdapting to diverse and unfamiliar teammates

Datasets & Benchmarks

Benchmarks
MPE • StarCraft II

Related Fields

Artificial IntelligenceMachine LearningRoboticsGame TheoryMulti-Agent Systems

Keywords

multi-agent reinforcement learningad hoc teamworkmulti-partycoordinationrelational modelinggraph neural networksunknown agentsMARLAHTMAHTMPEStarCraft II

Academic Context

#Multi-Agent Systems#Reinforcement Learning#Game Theory#Coordination

Commercial Potential

Potential Products
Advanced team coordination algorithms for robotsAI for complex strategy gamesSimulation platforms for multi-agent research
Target Industries
RoboticsGamingDefenseLogistics
Use Case Examples
A team of robots coordinating to explore an unknown environment with other unknown robot teams.AI agents in a real-time strategy game learning to cooperate with potentially unfamiliar allied units.
Competitive Edge
Extends the concept of ad hoc teamwork to a more complex multi-party setting, providing a novel approach to handle coordination with multiple unfamiliar agent groups.
Market Opportunity
Growing, with increasing interest in multi-agent AI for various applications.
Revenue Models
Licensing of algorithmsdevelopment of specialized AI for collaborative tasks.

Resource Requirements

Compute Needs
High, typical for multi-agent reinforcement learning training.
Data Requirements
Requires multi-agent simulation environments like MPE and StarCraft II.
Deployment Constraints
Scalability to very large numbers of agents can be challenging.,Real-world deployment requires robust communication and coordination mechanisms.
Scalability
The use of sparse graphs and relational modeling aims to improve scalability compared to dense interaction models.

Production Readiness

Maturity Level

Research

Time to Market

2-4 years

Patent Potential

Moderate, for the MARs algorithm and the MAHT framework.

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