A deep reinforcement learning model based on Node2vec-GraphSAGE for multi-agent path finding

doi:10.1088/1361-6501/ae425e

A deep reinforcement learning model based on Node2vec-GraphSAGE for multi-agent path finding

2026-02-18

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DOI：10.1088/1361-6501/ae425e

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Multi-agent path finding (MAPF) problem seeks collision-free and efficient paths for a set of agents from their start locations to designated goal locations, where effective inter-agent coordination is key. Advanced reinforcement learning (RL)-based MAPF planner often employ convolutional neural networks (CNN) to extract features from observed states; however, this approach typically fails to capture an efficient representation of the global topological structure. To address this limitation, we propose a novel graph modeling approach for MAPF. Within this framework, we explicitly model the observed state by constructing a NodeSAGE model. This model integrates node2vec-based fused-node random walks with a Graph sample and AggregatE network to capture graph structural features. Subsequently, decentralized policies are trained via RL augmented with expert knowledge. To enhance agent-environment coordination, the policy network incorporates dedicated attention-based modules: a static-obstacle aggregation module and a dynamic-obstacle aggregation module. Simulation experiments demonstrate that the learned policies achieve good performance.

Keywords:

Multi-agent path finding

GraphSAGE

Node2vec

Reinforcement learning

Attention mechanism

A deep reinforcement learning model based on Node2vec-GraphSAGE for multi-agent path finding

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