从信息素到策略：工程化生物群体的强化学习
From Pheromones to Policies: Reinforcement Learning for Engineered Biological Swarms
- Department of Computer Science, University of Namur（南姆大学计算机科学系）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究证明信息素介导的线虫聚集等价于强化学习，并通过引入探索性个体恢复动态环境中的集体可塑性，为工程化生物群体提供韧性决策框架。
AI中文摘要：
群体智能源于简单个体间的分散交互，从而实现集体问题求解。本研究建立了秀丽隐杆线虫中信息素介导的聚集与强化学习之间的理论等价性，证明了stigmergic信号（即通过环境间接协调个体行为的机制）如何作为分布式奖励机制发挥作用。我们模拟了执行觅食任务的工程化线虫群体，表明信息素动力学在数学上镜像了交叉学习更新——一种基础的强化学习算法。利用文献数据的实验验证证实，我们的模型在静态条件下能准确复现经验性的秀丽隐杆线虫觅食模式。在动态环境中，持久的信息素痕迹会产生正反馈回路，通过将群体锁定在过时选择中而阻碍适应性。通过多臂老虎机场景中的计算实验，我们揭示引入少数对信息素不敏感的探索性个体可恢复集体可塑性，从而实现快速任务切换。这种行为异质性平衡了探索-利用权衡，在群体层面实现了过时策略的灭绝。我们的结果表明，stigmergic系统内在地编码了分布式强化学习过程，其中环境信号充当外部记忆以进行集体信用分配。通过连接合成生物学与群体机器人学，这项工作推进了能够在动荡环境中做出韧性决策的可编程生命系统。
英文摘要：
Swarm intelligence emerges from decentralised interactions among simple agents, enabling collective problem-solving. This study establishes a theoretical equivalence between pheromone-mediated aggregation in \celeg\ and reinforcement learning (RL), demonstrating how stigmergic signals function as distributed reward mechanisms. We model engineered nematode swarms performing foraging tasks, showing that pheromone dynamics mathematically mirror cross-learning updates, a fundamental RL algorithm. Experimental validation with data from literature confirms that our model accurately replicates empirical \celeg\ foraging patterns under static conditions. In dynamic environments, persistent pheromone trails create positive feedback loops that hinder adaptation by locking swarms into obsolete choices. Through computational experiments in multi-armed bandit scenarios, we reveal that introducing a minority of exploratory agents insensitive to pheromones restores collective plasticity, enabling rapid task switching. This behavioural heterogeneity balances exploration-exploitation trade-offs, implementing swarm-level extinction of outdated strategies. Our results demonstrate that stigmergic systems inherently encode distributed RL processes, where environmental signals act as external memory for collective credit assignment. By bridging synthetic biology with swarm robotics, this work advances programmable living systems capable of resilient decision-making in volatile environments.
