BioRepairMARL: Multi-Agent-Assisted Biological Repair of Histological Systems

Tissue repair at the cellular level presents a formidable challenge due to the dynamic and stochastic nature of biological environments. Traditional therapeutic approaches, such as systemic drug delivery or surgical intervention, often lack precision and adaptability. In this work, we introduce a novel multi-agent reinforcement learning (MARL) framework for orchestrating autonomous micro-scale agents – synthetic nanorobots or engineered cells – to perform targeted tissue repair. Our approach integrates a biologically grounded model of agent dynamics, combining chemical diffusion, electrochemical signaling, and stochastic noise to enable decentralized coordination. Through in silico experiments, we demonstrate that the proposed MARL framework achieves stable repair performance, emergent coordination, and energy-efficient operation. The results highlight the potential of AI-driven biological agents to outperform traditional methods in precision and adaptability, paving the way for future clinical applications.