Multi-robot Information Sampling Using Deep Mean Field Reinforcement Learning
We study the problem of information sampling of an ambient phenomenon using a group of mobile robots. Autonomous robots are being deployed for various applications such as precision agriculture, search-and-rescue, among others. These robots are usually equipped with sensors and tasked with collecting maximal information for further data processing and decision making. The studied problem is proved to be NP-Hard in the literature. To solve the stated problem approximately, we employ a multi-agent deep reinforcement learning framework and use the concepts of mean field games to potentially scale the solution to larger multi-robot systems. Simulation results show that our presented technique easily scales to 10 robots in a 19 × 19 grid environment, while consistently sampling useful information.
Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Digital Object Identifier (DOI)
T. Said et al., "Multi-robot Information Sampling Using Deep Mean Field Reinforcement Learning," 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2021, pp. 1215-1220, doi: 10.1109/SMC52423.2021.9658795.