Achieving Optimal Tissue Repair Through MARL with Reward Shaping and Curriculum Learning
This work addresses tissue repair optimization for biomedical applications, representing an incremental advancement by combining existing MARL techniques with domain-specific biological modeling.
The paper tackles the problem of optimizing tissue repair by developing a multi-agent reinforcement learning framework that integrates stochastic reaction-diffusion systems, neural-like communication, and a biologically informed reward function, resulting in emergent repair strategies such as dynamic secretion control and spatial coordination in in silico experiments.
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.