# Cooperative Informative Sensing for Monitoring Dynamic Indoor Environments via Multi-Agent Reinforcement Learning

arXiv: https://arxiv.org/abs/2604.23179 · HTML: https://mlsanity.com/paper/2604.23179/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 28 references that are in the map.
- First posted on arXiv 25 Apr 2026.

## Builds on (most cited first, 10 of 10)

- [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021)
- [Human Motion Trajectory Prediction: A Survey](https://mlsanity.com/paper/1905.06113/) (2019)
- [The Complexity of Decentralized Control of Markov Decision Processes](https://mlsanity.com/paper/1301.3836/) (2013)
- [Adaptive Informative Path Planning Using Deep Reinforcement Learning for UAV-based Active Sensing](https://mlsanity.com/paper/2109.13570/) (2021)
- [Adaptive Informative Path Planning with Multimodal Sensing](https://mlsanity.com/paper/2003.09746/) (2020)
- [MSTC*:Multi-robot Coverage Path Planning under Physical Constraints](https://mlsanity.com/paper/2108.04632/) (2021)
- [Human Implicit Preference-Based Policy Fine-tuning for Multi-Agent Reinforcement Learning in USV Swarm](https://mlsanity.com/paper/2503.03796/) (2025)
- [Multi-Robot Active Information Gathering with Periodic Communication](https://mlsanity.com/paper/1703.02610/) (2017)
- [MARVEL: Multi-Agent Reinforcement Learning for constrained field-of-View multi-robot Exploration in Large-scale environments](https://mlsanity.com/paper/2502.20217/) (2025)
- [Priority-Aware Multi-Robot Coverage Path Planning](https://mlsanity.com/paper/2601.00580/) (2026)

## Abstract (arXiv)

Monitoring human activity in indoor environments is important for applications such as facility management, safety assessment, and space utilization analysis. While mobile robot teams offer the potential to actively improve observation quality, existing multi-robot monitoring and active perception approaches typically rely on coverage or visitation based objectives that are weakly aligned with the accuracy requirements of human-centric monitoring tasks. In this work, we formulate cooperative active observation as a decentralized control problem in which multiple robots adjust their motion to directly optimize monitoring accuracy under partial observability. We propose a learning-based framework for cooperative policies from decentralized observations using multi-agent reinforcement learning (MARL), supported by an architecture that handles variable numbers of humans and temporal dependencies. Simulation results across diverse indoor environments and monitoring tasks show that the proposed approach consistently outperforms classical coverage, persistent monitoring, and learning-free multi-robot baselines, while remaining robust to changes in the number of observed humans.