We investigate the emergence of cohesive flocking in open, boundless space using a multi-agent reinforcement learning framework. Agents integrate positional and orientational information from their closest topological neighbours and learn to balance alignment and attractive interactions by optimizing a local cost function that penalizes both excessive separation and close-range crowding. The resulting Vicsek-like dynamics is robust to algorithmic implementation details and yields cohesive collective motion with high polar order. The optimal policy is dominated by strong aligning interactions when agents are sufficiently close to their neighbours, and a flexible combination of alignment and attraction at larger separations. We further characterize the internal structure and dynamics of the resulting groups using liquid-state metrics and neighbour exchange rates, finding qualitative agreement with empirical observations in starling flocks. These results suggest that flocking may emerge in groups of moving agents as an adaptive response to the biological imperatives of staying together while avoiding collisions.

翻译：本研究采用多智能体强化学习框架，探究在开放无边界空间中凝聚性集群行为的涌现机制。智能体整合来自拓扑最近邻的位置与方向信息，通过优化局部成本函数（该函数同时惩罚过度分离与近距离拥挤）来学习平衡对齐与吸引相互作用。由此产生的类Vicsek动力学对算法实现细节具有鲁棒性，并能产生具有高极性序的凝聚性集体运动。最优策略表现为：当智能体与邻近个体足够接近时以强对齐相互作用为主导，在较大间距时则采用对齐与吸引作用的灵活组合。我们进一步利用液态指标和邻居交换率对生成群体的内部结构与动力学进行表征，发现其与椋鸟集群的经验观测结果具有定性一致性。这些结果表明，集群行为可能作为移动智能体群体对"保持聚集同时避免碰撞"这一生物必然需求的适应性响应而涌现。