Fluid-Agent Reinforcement Learning

2026-02-16

AI Summary

This work proposes a “fluid agents” framework that introduces agent generativity into multi-agent reinforcement learning, thereby relaxing the conventional assumption of a fixed population size. Traditional approaches struggle to handle dynamic group structures where agents may join, leave, or even be generated by other agents—common in real-world scenarios. By integrating game-theoretic modeling with tailored reinforcement learning algorithms, the framework enables emergent team formation and adaptation. Evaluated in extended Predator-Prey and Level-Based Foraging environments, the approach demonstrates the ability to train agent systems that autonomously adjust team size in response to task complexity. Experimental results show that this adaptive capability yields superior collaborative strategies and task performance compared to settings with fixed populations.

Technology Category

• Multiagent Systems: Teamwork
• Humans and AI: Teamwork, Team formation
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Search and Retrieval-Augmented AI: Agentic search

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.

Problem

• multi-agent reinforcement learning
• fluid-agent environment
• dynamic agent population
• agent creation
• variable number of agents

Innovation

• fluid-agent
• multi-agent reinforcement learning
• dynamic agent spawning
• game-theoretic solution
• adaptive team size