Deep Reinforcement Multi-agent Learning framework for Information Gathering with Local Gaussian Processes for Water Monitoring

TL;DR

This paper introduces a multi-agent system using Local Gaussian Processes and Deep Reinforcement Learning for efficient water quality monitoring, achieving significant accuracy improvements over existing methods.

Contribution

It presents a novel combination of Local Gaussian Processes with Deep Reinforcement Learning for multi-agent water monitoring, enhancing accuracy and safety.

Findings

Up to 24% reduction in mean absolute error with the proposed model.

20-24% smaller estimation errors compared to state-of-the-art approaches.

Effective coordination of autonomous vehicles for water quality assessment.

Abstract

The conservation of hydrological resources involves continuously monitoring their contamination. A multi-agent system composed of autonomous surface vehicles is proposed in this paper to efficiently monitor the water quality. To achieve a safe control of the fleet, the fleet policy should be able to act based on measurements and to the the fleet state. It is proposed to use Local Gaussian Processes and Deep Reinforcement Learning to jointly obtain effective monitoring policies. Local Gaussian processes, unlike classical global Gaussian processes, can accurately model the information in a dissimilar spatial correlation which captures more accurately the water quality information. A Deep convolutional policy is proposed, that bases the decisions on the observation on the mean and variance of this model, by means of an information gain reward. Using a Double Deep Q-Learning algorithm, agents are trained to minimize the estimation error in a safe manner thanks to a Consensus-based heuristic. Simulation results indicate an improvement of up to 24% in terms of the mean absolute error with the proposed models. Also, training results with 1-3 agents indicate that our proposed approach returns 20% and 24% smaller average estimation errors for, respectively, monitoring water quality variables and monitoring algae blooms, as compared to state-of-the-art approaches