Fluid-Agent Reinforcement Learning

• URL: http://arxiv.org/abs/2602.14559v1
• Date: Mon, 16 Feb 2026 08:37:46 GMT
• Title: Fluid-Agent Reinforcement Learning
• Abstract summary: In the real world, the number of agents is neither fixed nor known a priori.<n>We propose a framework that allows agents to create other agents; we call this a fluid-agent environment.<n>We present game-theoretic solution concepts for fluid-agent games and empirically evaluate the performance of several MARL algorithms within this framework.
• Score: 28.83171804545455
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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