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

arXiv: https://arxiv.org/abs/2401.04631 · HTML: https://mlsanity.com/paper/2401.04631/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 22 references that are in the map.
- First posted on arXiv 9 Jan 2024.

## Builds on (most cited first, 4 of 4)

- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [An informative path planning framework for UAV-based terrain monitoring](https://mlsanity.com/paper/1809.03870/) (2018)
- [Censored Deep Reinforcement Patrolling with Information Criterion for Monitoring Large Water Resources using Autonomous Surface Vehicles](https://mlsanity.com/paper/2210.08115/) (2022)
- [AquaFeL-PSO: A Monitoring System for Water Resources using Autonomous Surface Vehicles based on Multimodal PSO and Federated Learning](https://mlsanity.com/paper/2211.15217/) (2022)

## Built on by (most cited first, 7 of 7)

- [Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning](https://mlsanity.com/paper/2412.02316/) (2024)
- [Scalable Multi-Robot Informative Path Planning for Target Mapping via Deep Reinforcement Learning](https://mlsanity.com/paper/2409.16967/) (2024)
- [Uncertainty Estimation for Deep Reconstruction in Actuatic Disaster Scenarios with Autonomous Vehicles](https://mlsanity.com/paper/2604.06387/) (2026)
- [Towards an Autonomous Surface Vehicle Prototype for Artificial Intelligence Applications of Water Quality Monitoring](https://mlsanity.com/paper/2410.05892/) (2024)
- [A Unified Experimental Architecture for Informative Path Planning: from Simulation to Deployment with GuadalPlanner](https://mlsanity.com/paper/2602.10702/) (2026)
- [Robust and Efficient Communication for Multi-Agent Learning](https://mlsanity.com/paper/2609.15361/) (2026)
- [Calibrated Uncertainty for Informative Path Planning in Aquatic Environmental Monitoring](https://mlsanity.com/paper/2609.34577/) (2026)

## Abstract (arXiv)

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