通过带有奖励塑造和课程学习的MARL实现最优组织修复
Achieving Optimal Tissue Repair Through MARL with Reward Shaping and Curriculum Learning
- College of Interdisciplinary Studies, Zayed University（扎耶德大学跨学科研究学院）
- College of Engineering, The Hashemite University（哈希米特大学工程学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出集成随机反应扩散系统、类神经电化学通信和生物启发奖励函数的MARL框架，结合课程学习优化组织修复，硅基实验展现动态分泌控制等涌现修复策略。
AI中文摘要：
本文提出一种多智能体强化学习（MARL）框架，用于利用工程化生物智能体优化组织修复过程。我们的方法集成：（1）建模分子信号的随机反应扩散系统；（2）具有赫布可塑性的类神经电化学通信；（3）结合化学梯度跟踪、神经同步和鲁棒惩罚的生物启发奖励函数。课程学习方案引导智能体经历逐步复杂的修复场景。硅基实验展示了包括动态分泌控制和空间协调在内的涌现修复策略。
英文摘要：
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.
