# A Structured Prediction Approach for Generalization in Cooperative Multi-Agent Reinforcement Learning

arXiv: https://arxiv.org/abs/1910.08809 · HTML: https://mlsanity.com/paper/1910.08809/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2019.

## Built on by (most cited first, 10 of 10)

- [Towards a Standardised Performance Evaluation Protocol for Cooperative MARL](https://mlsanity.com/paper/2209.10485/) (2022)
- [Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team Composition](https://mlsanity.com/paper/2105.08692/) (2021)
- [ALMA: Hierarchical Learning for Composite Multi-Agent Tasks](https://mlsanity.com/paper/2205.14205/) (2022)
- [Cross-environment Cooperation Enables Zero-shot Multi-agent Coordination](https://mlsanity.com/paper/2504.12714/) (2025)
- [The Overcooked Generalisation Challenge: Evaluating Cooperation with Novel Partners in Unknown Environments Using Unsupervised Environment Design](https://mlsanity.com/paper/2406.17949/) (2024)
- [Multi-Agent Reinforcement Learning: Methods, Applications, Visionary Prospects, and Challenges](https://mlsanity.com/paper/2305.10091/) (2023)
- [RPM: Generalizable Behaviors for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2210.09646/) (2022)
- [Trajectory-Class-Aware Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2503.01440/) (2025)
- [Bidirectional Distillation: A Mixed-Play Framework for Multi-Agent Generalizable Behaviors](https://mlsanity.com/paper/2505.11100/) (2025)
- [Embedded Safety-Aligned Intelligence via Differentiable Internal Alignment Embeddings](https://mlsanity.com/paper/2512.18309/) (2025)

## Abstract (arXiv)

Effective coordination is crucial to solve multi-agent collaborative (MAC) problems. While centralized reinforcement learning methods can optimally solve small MAC instances, they do not scale to large problems and they fail to generalize to scenarios different from those seen during training. In this paper, we consider MAC problems with some intrinsic notion of locality (e.g., geographic proximity) such that interactions between agents and tasks are locally limited. By leveraging this property, we introduce a novel structured prediction approach to assign agents to tasks. At each step, the assignment is obtained by solving a centralized optimization problem (the inference procedure) whose objective function is parameterized by a learned scoring model. We propose different combinations of inference procedures and scoring models able to represent coordination patterns of increasing complexity. The resulting assignment policy can be efficiently learned on small problem instances and readily reused in problems with more agents and tasks (i.e., zero-shot generalization). We report experimental results on a toy search and rescue problem and on several target selection scenarios in StarCraft: Brood War, in which our model significantly outperforms strong rule-based baselines on instances with 5 times more agents and tasks than those seen during training.