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[2602.14559] Fluid-Agent Reinforcement Learning
Summary
The paper introduces a novel framework for multi-agent reinforcement learning (MARL) that allows agents to create other agents, termed fluid-agent environments, enhancing adaptability in dynamic scenarios.
Why It Matters
This research addresses a significant gap in MARL by recognizing that real-world scenarios often involve variable agent populations. The proposed fluid-agent framework could lead to more efficient and scalable solutions in complex environments, impacting fields like robotics and AI systems.
Key Takeaways
- Introduces fluid-agent environments where agents can spawn others.
- Demonstrates the potential for dynamic team sizes to adapt to environmental demands.
- Empirically evaluates MARL algorithms within this new framework.
- Highlights novel solution strategies that emerge in fluid settings.
- Contributes to the understanding of agent interactions in non-fixed populations.
Computer Science > Machine Learning arXiv:2602.14559 (cs) [Submitted on 16 Feb 2026] Title:Fluid-Agent Reinforcement Learning Authors:Shishir Sharma, Doina Precup, Theodore J. Perkins View a PDF of the paper titled Fluid-Agent Reinforcement Learning, by Shishir Sharma and 1 other authors View PDF HTML (experimental) Abstract:The primary focus of multi-agent reinforcement learning (MARL) has been to study interactions among a fixed number of agents embedded in an environment. However, in the real world, the number of agents is neither fixed nor known a priori. Moreover, an agent can decide to create other agents (for example, a cell may divide, or a company may spin off a division). In this paper, we propose a framework that allows agents to create other agents; we call this a fluid-agent environment. We present game-theoretic solution concepts for fluid-agent games and empirically evaluate the performance of several MARL algorithms within this framework. Our experiments include fluid variants of established benchmarks such as Predator-Prey and Level-Based Foraging, where agents can dynamically spawn, as well as a new environment we introduce that highlights how fluidity can unlock novel solution strategies beyond those observed in fixed-population settings. We demonstrate that this framework yields agent teams that adjust their size dynamically to match environmental demands. Comments: Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Multiagent Systems ...