Ensembling Prioritized Hybrid Policies for Multi-agent Pathfinding
Huijie Tang Federico Berto Jinkyoo Park
Multi-Agent Reinforcement Learning (MARL) based Multi-Agent Path Finding
(MAPF) has recently gained attention due to its efficiency and scalability.
Several MARL-MAPF methods choose to use communication to enrich the information
one agent can perceive. However, existing works still struggle in structured
environments with high obstacle density and a high number of agents. To further
improve the performance of the communication-based MARL-MAPF solvers, we
propose a new method, Ensembling Prioritized Hybrid Policies (EPH). We first
propose a selective communication block to gather richer information for better
agent coordination within multi-agent environments and train the model with a
Q-learning-based algorithm. We further introduce three advanced inference
strategies aimed at bolstering performance during the execution phase. First,
we hybridize the neural policy with single-agent expert guidance for navigating
conflict-free zones. Secondly, we propose Q value-based methods for prioritized
resolution of conflicts as well as deadlock situations. Finally, we introduce a
robust ensemble method that can efficiently collect the best out of multiple
possible solutions. We empirically evaluate EPH in complex multi-agent
environments and demonstrate competitive performance against state-of-the-art
neural methods for MAPF.