Sparse shepherding control of large-scale multi-agent systems via Reinforcement Learning
For researchers in multi-agent systems and control, this work provides a scalable learning-based approach to sparse control, though it is incremental over existing methods.
This paper introduces a Reinforcement Learning framework for sparse control of large-scale multi-agent systems, where a few controlled agents steer many uncontrolled ones. The method achieves effective density control with robustness to disturbances, replacing computationally expensive online optimization.
We propose a Reinforcement Learning framework for sparse indirect control of large-scale multi-agent systems, where few controlled agents shape the collective behavior of many uncontrolled agents. The approach addresses this multi-scale challenge by coupling ODEs (modeling controlled agents) with a PDE (describing the uncontrolled population density), capturing how microscopic control achieves macroscopic objectives. Our method combines model-free Reinforcement Learning with adaptive interaction strength compensation to overcome sparse actuation limitations. Numerical validation demonstrates effective density control, with the system achieving target distributions while maintaining robustness to disturbances and measurement noise, confirming that learning-based sparse control can replace computationally expensive online optimization.