Predicting Multi-Agent Specialization via Task Parallelizability
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915500001329152 |
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| author | Mieczkowski, Elizabeth Mon-Williams, Ruaridh Bramley, Neil Lucas, Christopher G. Velez, Natalia Griffiths, Thomas L. |
| author_facet | Mieczkowski, Elizabeth Mon-Williams, Ruaridh Bramley, Neil Lucas, Christopher G. Velez, Natalia Griffiths, Thomas L. |
| contents | When should we encourage specialization in multi-agent systems versus train generalists that perform the entire task independently? We propose that specialization largely depends on task parallelizability: the potential for multiple agents to execute task components concurrently. Drawing inspiration from Amdahl's Law in distributed systems, we present a closed-form bound that predicts when specialization improves performance, depending only on task concurrency and team size. We validate our model on two standard MARL benchmarks that represent opposite regimes -- StarCraft Multi-Agent Challenge (SMAC, unlimited concurrency) and Multi-Particle Environment (MPE, unit-capacity bottlenecks) -- and observe close alignment between the bound at each extreme and an empirical measure of specialization. Three follow-up experiments in Overcooked-AI demonstrate that the model works in environments with more complex spatial and resource bottlenecks that allow for a range of strategies. Beyond prediction, the bound also serves as a diagnostic tool, highlighting biases in MARL training algorithms that cause sub-optimal convergence to specialist strategies with larger state spaces. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_15703 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Predicting Multi-Agent Specialization via Task Parallelizability Mieczkowski, Elizabeth Mon-Williams, Ruaridh Bramley, Neil Lucas, Christopher G. Velez, Natalia Griffiths, Thomas L. Multiagent Systems Artificial Intelligence When should we encourage specialization in multi-agent systems versus train generalists that perform the entire task independently? We propose that specialization largely depends on task parallelizability: the potential for multiple agents to execute task components concurrently. Drawing inspiration from Amdahl's Law in distributed systems, we present a closed-form bound that predicts when specialization improves performance, depending only on task concurrency and team size. We validate our model on two standard MARL benchmarks that represent opposite regimes -- StarCraft Multi-Agent Challenge (SMAC, unlimited concurrency) and Multi-Particle Environment (MPE, unit-capacity bottlenecks) -- and observe close alignment between the bound at each extreme and an empirical measure of specialization. Three follow-up experiments in Overcooked-AI demonstrate that the model works in environments with more complex spatial and resource bottlenecks that allow for a range of strategies. Beyond prediction, the bound also serves as a diagnostic tool, highlighting biases in MARL training algorithms that cause sub-optimal convergence to specialist strategies with larger state spaces. |
| title | Predicting Multi-Agent Specialization via Task Parallelizability |
| topic | Multiagent Systems Artificial Intelligence |
| url | https://arxiv.org/abs/2503.15703 |