Achieving Optimal Tissue Repair Through MARL with Reward Shaping and Curriculum Learning
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.
Keywords
Cite
@article{arxiv.2504.10677,
title = {Achieving Optimal Tissue Repair Through MARL with Reward Shaping and Curriculum Learning},
author = {Muhammad Al-Zafar Khan and Jamal Al-Karaki},
journal= {arXiv preprint arXiv:2504.10677},
year = {2025}
}
Comments
14 pages, 4 figures, submitted to the 10th International Conference on Information and Communication Technology for Intelligent Systems (ICTIS)