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

arXiv 2504.10677 ↗First posted 14 Apr 2025cs.LG cs.AI cs.MAcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 21 references that are in the map. First posted on arXiv 14 Apr 2025.

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References · 2

  1. 2020Deep Reinforcement Learning for Intelligent Transportation Systems: A Surveycited by 98
  2. 2018Multi-Agent Reinforcement Learning Based Resource Allocation for UAV Networkscited by 56

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