Multi-robot Information Sampling Using Deep Mean Field Reinforcement Learning

Tuffa Said, Jeffery Wolbert, Siavash Khodadadeh, Ayan Dutta, O. Patrick Kreidl, Ladislau Bölöni, Swapnoneel Roy

Abstract: 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.