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Multi-agent path finding in formation has many potential real-worldapplications like mobile warehouse robots. However, previous multi-agent pathfinding (MAPF) methods hardly take formation into consideration. Furthermore,they are usually centralized planners and require the whole state of theenvironment. Other decentralized partially observable approaches to MAPF arereinforcement learning (RL) methods. However, these RL methods encounterdifficulties when learning path finding and formation problem at the same time.In this paper, we propose a novel decentralized partially observable RLalgorithm that uses a hierarchical structure to decompose the multi objectivetask into unrelated ones. It also calculates a theoretical weight that makesevery task reward has equal influence on the final RL value function.Additionally, we introduce a communication method that helps agents cooperatewith each other. Experiments in simulation show that our method outperformsother end-to-end RL methods and our method can naturally scale to large worldsizes where centralized planner struggles. We also deploy and validate ourmethod in a real world scenario.

Moving Forward in Formation: A Decentralized Hierarchical Learning Approach to Multi-Agent Moving Together