Modular Reinforcement Learning For Cooperative Swarms
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866915984039739392 |
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| author | Shtossel, Erel Kaminka, Gal A. |
| author_facet | Shtossel, Erel Kaminka, Gal A. |
| contents | A cooperative robot swarm is a collective of computationally-limited robots that share a common goal. Each robot can only interact with a small subset of its peers, without knowing how this affects the collective utility. Recent advances in distributed multi-agent reinforcement learning have demonstrated that it is possible for robots to learn how to interact effectively with others, in a manner that is aligned with the common goal, despite each robot learning independently of others. However, this requires each robot to represent a potentially combinatorial number of interaction states, challenging the memory capabilities of the robots. This paper proposes an alternative approach for representing spatial interaction states for multi-robot reinforcement learning in swarms. A modular (decomposed) representation is used, where each feature of the state is handled by a separate learning procedure, and the results aggregated. We demonstrate the efficacy of the approach in numerous experiments with simulated robot swarms carrying out foraging. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_04939 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Modular Reinforcement Learning For Cooperative Swarms Shtossel, Erel Kaminka, Gal A. Robotics Artificial Intelligence A cooperative robot swarm is a collective of computationally-limited robots that share a common goal. Each robot can only interact with a small subset of its peers, without knowing how this affects the collective utility. Recent advances in distributed multi-agent reinforcement learning have demonstrated that it is possible for robots to learn how to interact effectively with others, in a manner that is aligned with the common goal, despite each robot learning independently of others. However, this requires each robot to represent a potentially combinatorial number of interaction states, challenging the memory capabilities of the robots. This paper proposes an alternative approach for representing spatial interaction states for multi-robot reinforcement learning in swarms. A modular (decomposed) representation is used, where each feature of the state is handled by a separate learning procedure, and the results aggregated. We demonstrate the efficacy of the approach in numerous experiments with simulated robot swarms carrying out foraging. |
| title | Modular Reinforcement Learning For Cooperative Swarms |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2605.04939 |