Swarm robotic systems are a novel approach to the coordination of large numbers of robots. Swarms bring desirable properties like robustness, flexibility, and scalability to robotic systems. These systems could be deployed to applications such as on-demand wireless networks, distributed mapping, large-scale localization, environmental monitoring, etc. Swarm robotics differentiates itself from multi-robot systems in the scale of robot group size and simplicity of individual robots to emerge collective intelligence. Incapability and communication limitation of individual robots introduce the challenge for scalable control strategy design. In this talk, I aim to present my previous work on learning a decentralized, scalable strategy using knowledge-based neural ordinary differential equations. In the second part, I will present an approach to scalable environmental monitoring, which is empirically proven robust to robot and communication failure, using multi-agent reinforcement learning.