Achieving Optimal Tissue Repair Through MARL with Reward Shaping and Curriculum Learning

2025-04-14

AI Summary

This study addresses the low coordination efficiency and poor robustness of engineered biological agents in tissue repair. We propose a biologically inspired multi-agent reinforcement learning (MARL) framework. Methodologically, we introduce— for the first time—the integration of reaction-diffusion modeling, Hebbian-like neurochemical communication, and a tripartite reward mechanism based on gradient sensing, synchronization, and robustness; additionally, we incorporate a molecular signal-driven curriculum learning strategy. Unlike conventional MARL, our paradigm explicitly models molecular diffusion dynamics and biologically plausible synaptic plasticity, enabling emergent behaviors such as dynamic secretion regulation and spatial coordination. In silico experiments demonstrate a 37% improvement in repair efficiency and significantly enhanced robustness against noise and localized damage. This work establishes the first interpretable and scalable control paradigm for cooperative biological agents, bridging synthetic biology and intelligent healthcare.

Technology Category

• Multiagent Systems: Multiagent Learning
• Machine Learning: Bio-inspired Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

In this paper, we present a multi-agent reinforcement learning (MARL) framework for optimizing tissue repair processes using engineered biological agents. Our approach integrates: (1) stochastic reaction-diffusion systems modeling molecular signaling, (2) neural-like electrochemical communication with Hebbian plasticity, and (3) a biologically informed reward function combining chemical gradient tracking, neural synchronization, and robust penalties. A curriculum learning scheme guides the agent through progressively complex repair scenarios. In silico experiments demonstrate emergent repair strategies, including dynamic secretion control and spatial coordination.

Problem

• Optimizing tissue repair using multi-agent reinforcement learning
• Modeling molecular signaling with reaction-diffusion systems
• Developing biologically informed reward functions for repair

Innovation

• MARL framework with reward shaping
• Stochastic reaction-diffusion molecular modeling
• Neural-like electrochemical communication system