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arXiv · 2504.10677

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

Muhammad Al-Zafar Khan·Jamal Al-Karaki
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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.

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Muhammad Al-Zafar Khan, Jamal Al-Karaki. 2025-04-14. Achieving Optimal Tissue Repair Through MARL with Reward Shaping and Curriculum Learning. https://arxiv.org/abs/2504.10677

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