Uw-Marl

Abstract

Near-real-time water-quality monitoring in uncertain environments such as rivers, lakes, and water reservoirs of different variables is critical to protect the aquatic life and to prevent further propagation of the potential pollution in the water. In order to measure the physical values in a region of interest, adaptive sampling is helpful as an energy-and time-efficient technique since an exhaustive search of an area is not feasible with a single vehicle. We propose an adaptive sampling algorithm using multiple autonomous vehicles, which are well-trained, as agents, in a Multi-Agent Reinforcement Learning (MARL) framework to make efficient sequence of decisions on the adaptive sampling procedure. The proposed solution is evaluated using experimental data, which is fed into a simulation framework. Experiments were conducted in the Raritan River, Somerset and in Carnegie Lake, Princeton, NJ during July 2019.

A survey on autonomous environmental monitoring approaches: towards unifying active sensing and reinforcement learning

“…This is a valuable corrective action as When the team is submerged it is unable to communicate and apply corrections until the communication is available again. Or RL has been used for processing acoustic signals which are a method of underwater communication Rahmati et al (2019) . MARL can also be used to teach agents how to cooperate without or with very limited communication Zhang and Lesser (2013) .…”