  The primary objective of Multi-Agent Pathfinding (MAPF) is to plan efficient
and conflict-free paths for all agents. Traditional multi-agent path planning
algorithms struggle to achieve efficient distributed path planning for multiple
agents. In contrast, Multi-Agent Reinforcement Learning (MARL) has been
demonstrated as an effective approach to achieve this objective. By modeling
the MAPF problem as a MARL problem, agents can achieve efficient path planning
and collision avoidance through distributed strategies under partial
observation. However, MARL strategies often lack cooperation among agents due
to the absence of global information, which subsequently leads to reduced MAPF
efficiency. To address this challenge, this letter introduces a unique reward
shaping technique based on Independent Q-Learning (IQL). The aim of this method
is to evaluate the influence of one agent on its neighbors and integrate such
an interaction into the reward function, leading to active cooperation among
agents. This reward shaping method facilitates cooperation among agents while
operating in a distributed manner. The proposed approach has been evaluated
through experiments across various scenarios with different scales and agent
counts. The results are compared with those from other state-of-the-art (SOTA)
planners. The evidence suggests that the approach proposed in this letter
parallels other planners in numerous aspects, and outperforms them in scenarios
featuring a large number of agents.
