  Planning coverage path for multiple robots in a decentralized way enhances
robustness to coverage tasks handling uncertain malfunctions. To achieve high
efficiency in a distributed manner for each single robot, a comprehensive
understanding of both the complicated environments and cooperative agents
intent is crucial. Unfortunately, existing works commonly consider only part of
these factors, resulting in imbalanced subareas or unnecessary overlaps. To
tackle this issue, we introduce a Decentralized reinforcement learning
framework with dual guidance to train each agent to solve the decentralized
multiple coverage path planning problem straightly through the environment
states. As distributed robots require others intentions to perform better
coverage efficiency, we utilize two guidance methods, artificial potential
fields and heuristic guidance, to include and integrate others intentions into
observations for each robot. With our constructed framework, results have shown
our agents successfully learn to determine their own subareas while achieving
full coverage, balanced subareas and low overlap rates. We then implement
spanning tree cover within those subareas to construct actual routes for each
robot and complete given coverage tasks. Our performance is also compared with
the state of the art decentralized method showing at most 10 percent lower
overlap rates while performing high efficiency in similar environments.
