Scholarlink

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.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Muhammad Al-Zafar Khan,
  • Jamal Al-Karaki,
  • Marwan Omar

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.