# N-Agent Ad Hoc Teamwork

arXiv: https://arxiv.org/abs/2404.10740 · HTML: https://mlsanity.com/paper/2404.10740/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods.
- Builds on 31 of its 51 references that are in the map.
- First posted on arXiv 16 Apr 2024.

## Placed in

- Deep reinforcement learning methods / [RL for games and combinatorial optimization](https://mlsanity.com/deeprl/rl-for-games-and-combinatorial-optimization/)

## Builds on (most cited first, 30 of 31)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Counterfactual Multi-Agent Policy Gradients](https://mlsanity.com/paper/1705.08926/) (2017)
- [QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1803.11485/) (2018)
- [The StarCraft Multi-Agent Challenge](https://mlsanity.com/paper/1902.04043/) (2019)
- [QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1905.05408/) (2019)
- [Emergent Tool Use From Multi-Agent Autocurricula](https://mlsanity.com/paper/1909.07528/) (2019)
- [The Complexity of Decentralized Control of Markov Decision Processes](https://mlsanity.com/paper/1301.3836/) (2013)
- [Learning with Opponent-Learning Awareness](https://mlsanity.com/paper/1709.04326/) (2017)
- [Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks](https://mlsanity.com/paper/2006.07869/) (2020)
- ["Other-Play" for Zero-Shot Coordination](https://mlsanity.com/paper/2003.02979/) (2020)
- [A Survey and Critique of Multiagent Deep Reinforcement Learning](https://mlsanity.com/paper/1810.05587/) (2018)
- [Graph Convolutional Reinforcement Learning](https://mlsanity.com/paper/1810.09202/) (2018)
- [Collaborating with Humans without Human Data](https://mlsanity.com/paper/2110.08176/) (2021)
- [Autonomous Agents Modelling Other Agents: A Comprehensive Survey and Open Problems](https://mlsanity.com/paper/1709.08071/) (2017)
- [Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot](https://mlsanity.com/paper/2107.06857/) (2021)
- [Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing](https://mlsanity.com/paper/2102.07475/) (2021)
- [Agent Modelling under Partial Observability for Deep Reinforcement Learning](https://mlsanity.com/paper/2006.09447/) (2020)
- [Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1912.02288/) (2019)
- [A Survey of Ad Hoc Teamwork Research](https://mlsanity.com/paper/2202.10450/) (2022)
- [Melting Pot 2.0](https://mlsanity.com/paper/2211.13746/) (2022)
- [A Survey of Progress on Cooperative Multi-agent Reinforcement Learning in Open Environment](https://mlsanity.com/paper/2312.01058/) (2023)
- [Towards Open Ad Hoc Teamwork Using Graph-based Policy Learning](https://mlsanity.com/paper/2006.10412/) (2020)
- [Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team Composition](https://mlsanity.com/paper/2105.08692/) (2021)
- [Deep Interactive Bayesian Reinforcement Learning via Meta-Learning](https://mlsanity.com/paper/2101.03864/) (2021)
- [TiZero: Mastering Multi-Agent Football with Curriculum Learning and Self-Play](https://mlsanity.com/paper/2302.07515/) (2023)
- [LDSA: Learning Dynamic Subtask Assignment in Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2205.02561/) (2022)
- [Interpretable Goal Recognition in the Presence of Occluded Factors for Autonomous Vehicles](https://mlsanity.com/paper/2108.02530/) (2021)
- [A General Learning Framework for Open Ad Hoc Teamwork Using Graph-based Policy Learning](https://mlsanity.com/paper/2210.05448/) (2022)

## Built on by (most cited first, 15 of 15)

- [Inherent and emergent liability issues in LLM-based agentic systems: a principal-agent perspective](https://mlsanity.com/paper/2504.03255/) (2025)
- [Seven Security Challenges in Cross-domain Multi-agent LLM Systems](https://mlsanity.com/paper/2505.23847/) (2025)
- [ROTATE: Regret-driven Open-ended Training for Ad Hoc Teamwork](https://mlsanity.com/paper/2505.23686/) (2025)
- [Shapley Machine: A Game-Theoretic Framework for N-Agent Ad Hoc Teamwork](https://mlsanity.com/paper/2506.11285/) (2025)
- [Multi-Agent Reinforcement Learning Simulation for Environmental Policy Synthesis](https://mlsanity.com/paper/2504.12777/) (2025)
- [Breaking the Performance Ceiling in Reinforcement Learning requires Inference Strategies](https://mlsanity.com/paper/2505.21236/) (2025)
- [Unifying Agent Interaction and World Information for Multi-agent Coordination](https://mlsanity.com/paper/2509.25550/) (2025)
- [Shaping Zero-Shot Coordination via State Blocking](https://mlsanity.com/paper/2605.11688/) (2026)
- [Zero-Shot Coordination in Ad Hoc Teams with Generalized Policy Improvement and Difference Rewards](https://mlsanity.com/paper/2510.16187/) (2025)
- [Rollout Cards: A Reproducibility Standard for Agent Research](https://mlsanity.com/paper/2605.12131/) (2026)
- [JaxAHT: A JAX-Based Library for Ad Hoc Teamwork](https://mlsanity.com/paper/2609.13716/) (2026)
- [AT-Drone: Benchmarking Adaptive Teaming in Multi-Drone Pursuit](https://mlsanity.com/paper/2502.09762/) (2025)
- [ReCollab: Retrieval-Augmented LLMs for Cooperative Ad-hoc Teammate Modeling](https://mlsanity.com/paper/2512.22129/) (2025)
- [MOSAIC: A Universal Agent-Level Interface for Cross-Paradigm Agent Mixing and Human-AI Collaboration](https://mlsanity.com/paper/2603.01260/) (2026)
- [Predicting Decisions of AI Agents from Limited Interaction through Text-Tabular Modeling](https://mlsanity.com/paper/2605.12411/) (2026)

## Abstract (arXiv)

Current approaches to learning cooperative multi-agent behaviors assume relatively restrictive settings. In standard fully cooperative multi-agent reinforcement learning, the learning algorithm controls textitall agents in the scenario, while in ad hoc teamwork, the learning algorithm usually assumes control over only a textitsingle agent in the scenario. However, many cooperative settings in the real world are much less restrictive. For example, in an autonomous driving scenario, a company might train its cars with the same learning algorithm, yet once on the road, these cars must cooperate with cars from another company. Towards expanding the class of scenarios that cooperative learning methods may optimally address, we introduce N-agent ad hoc teamwork (NAHT), where a set of autonomous agents must interact and cooperate with dynamically varying numbers and types of teammates. This paper formalizes the problem, and proposes the Policy Optimization with Agent Modelling (POAM) algorithm. POAM is a policy gradient, multi-agent reinforcement learning approach to the NAHT problem, that enables adaptation to diverse teammate behaviors by learning representations of teammate behaviors. Empirical evaluation on tasks from the multi-agent particle environment and StarCraft II shows that POAM improves cooperative task returns compared to baseline approaches, and enables out-of-distribution generalization to unseen teammates.