Scalable MARL for Cooperative Exploration with Dynamic Robot Populations via Graph-Based Information Aggregation

Abstract

This study addresses the challenge of multi-robot cooperative exploration under limited local observations in environments with dynamic robot populations. To achieve efficient area coverage within constrained timeframes, we propose the Multi-Robot Informative Planner (MIP), a novel reinforcement learning (RL)-based planning module. The core component of MIP is the Neighborhood Information Aggregator, which employs a graph neural network (GNN) to integrate local neighborhood information for each robot. Our design enhances sample efficiency by minimizing information requirements while ensuring scalability across environments with varying robot numbers. To generate high-quality, expressive neighborhood feature representations, we utilize Graphical Mutual Information (GMI) to maximize the correlation between neighboring robots’ input features and their high-level hidden representations. Furthermore, MIP incorporates the Spatial-Neighborhood Transformer, which captures spatial features and inter-robot interactions through spatial self-attention mechanisms. These components collectively form the Multi-Robot Neural Informative Mapping (MRNIM) framework, outperforming traditional benchmarks in Habitat simulator.