Can MARL optimize tissue repair using LLMs?
Achieving Optimal Tissue Repair Through MARL with Reward Shaping and Curriculum Learning
This research explores using a multi-agent reinforcement learning (MARL) system to simulate and optimize tissue repair. The agents (engineered cells) interact within a simulated biological environment, learning to secrete healing factors and navigate towards injury sites, guided by a reward system that incentivizes efficient healing and penalizes damage. A curriculum learning approach gradually increases the complexity of the repair scenarios.
Key points for LLM-based multi-agent systems: The paper highlights the challenges of applying MARL in biological settings (heterogeneity, partial observability, asynchronous actions, delayed rewards) and offers solutions such as biologically-inspired reward shaping, curriculum learning, and a hybrid chemical-neural signaling model. These concepts are relevant to complex LLM-based multi-agent scenarios where agents need to coordinate in dynamic, partially observable environments with delayed feedback. The stochastic reaction-diffusion system for communication and the focus on reward shaping could inspire similar mechanisms in LLM agent interaction.