# Learning Altruistic Collaboration in Heterogeneous Multi-Team Systems

arXiv: https://arxiv.org/abs/2605.21723 · HTML: https://mlsanity.com/paper/2605.21723/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 31 references that are in the map.
- First posted on arXiv 20 May 2026.

## Builds on (most cited first, 12 of 12)

- [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018)
- [Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon](https://mlsanity.com/paper/1811.06128/) (2018)
- [Learning Combinatorial Optimization Algorithms over Graphs](https://mlsanity.com/paper/1704.01665/) (2017)
- [Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2003.08839/) (2020)
- [Graph networks as learnable physics engines for inference and control](https://mlsanity.com/paper/1806.01242/) (2018)
- [Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning](https://mlsanity.com/paper/2010.12367/) (2020)
- [Graph Neural Networks for Decentralized Multi-Robot Path Planning](https://mlsanity.com/paper/1912.06095/) (2019)
- [Learning Decentralized Controllers for Robot Swarms with Graph Neural Networks](https://mlsanity.com/paper/1903.10527/) (2019)
- [A Resilient and Energy-Aware Task Allocation Framework for Heterogeneous Multi-Robot Systems](https://mlsanity.com/paper/2105.05586/) (2021)
- [Collaboration in Multi-Robot Systems: Taxonomy and Survey over Frameworks for Collaboration](https://mlsanity.com/paper/2603.23898/) (2026)
- [Resource Allocation with Multi-Team Collaboration Based on Hamilton's Rule](https://mlsanity.com/paper/2505.09016/) (2025)
- [Hamilton's Rule for Enabling Altruism in Multi-Agent Systems](https://mlsanity.com/paper/2505.09841/) (2025)

## Built on by (most cited first, 1 of 1)

- [MemoGuard: An Adaptive Runtime for Guarding Against Memory Traps in Communication-Limited Robot Navigation](https://mlsanity.com/paper/2607.15589/) (2026)

## Abstract (arXiv)

This paper studies heterogeneous multi-team collaboration through dynamic robot allocation, where robots are treated as transferable resources. Leveraging Hamilton's rule from ecology as an altruistic decision-making mechanism, we propose a multi-team collaborative resource allocation framework with heterogeneous capabilities, transfer costs, and capability-dependent contributions. The resulting allocation problem is combinatorial and is shown to be NP-hard. To address scalability, we develop a graph neural network policy under centralized training and decentralized execution that approximates the altruistic allocations based on Hamilton's rule. The model operates over the team interaction graph and predicts robot-level transfer decisions and next robot-to-team assignments. The proposed approach is validated in a firefighting scenario through simulations and experiments, demonstrating that the learned policy achieves near-optimal performance while scaling to larger systems.