用多智能体强化学习优化组织修复，实现动态分泌与空间协同。
Achieving Optimal Tissue Repair Through MARL with Reward Shaping and Curriculum Learning
- 通过生物启发的奖励函数与课程学习，引导智能体逐步掌握修复策略。
- 在模拟中实现动态分泌调控与空间协调，修复效率显著提升。
- 适合生物医学工程与智能药物设计研究者参考。
本文提出一种基于多智能体强化学习（MARL）的组织修复优化框架，利用工程化生物剂实现修复过程的智能化控制。方法融合随机反应-扩散系统建模分子信号传递、类神经电化学通信机制及赫布可塑性，并设计结合化学梯度追踪、神经同步与鲁棒惩罚的生物启发式奖励函数。通过课程学习策略，引导智能体逐步应对复杂修复场景。仿真实验揭示了自组织修复行为，包括动态分泌调控与空间协同策略，验证了该框架在复杂生物系统中的有效性。
原文摘要 · Abstract (English)
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.
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