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

Author First name, Last name, Institution

Muhammad Al Zafar Khan, Zayed University
Jamal Al-Karaki, Zayed University
Marwan Omar, College of Computing

Document Type

Conference Proceeding

Source of Publication

Smart Innovation Systems and Technologies

Publication Date

1-2-2026

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.

DOI Link

10.1007/978-981-95-1361-1_22

ISBN

[9789819513604]

ISSN

2190-3018

Publisher

Springer Nature Singapore

Volume

126 SIST

First Page

281

Last Page

290

Disciplines

Computer Sciences

Keywords

Computational biology, Intelligent tissue repair, Multi-agent Reinforcement Learning (MARL)

Scopus ID

105028283049

Recommended Citation

Khan, Muhammad Al Zafar; Al-Karaki, Jamal; and Omar, Marwan, "Achieving Optimal Tissue Repair Through MARL with Reward Shaping and Curriculum Learning" (2026). All Works. 7736.
https://zuscholars.zu.ac.ae/works/7736

Indexed in Scopus

yes

Open Access

no