  Cooperative multi-robot teams need to be able to explore cluttered and
unstructured environments while dealing with communication dropouts that
prevent them from exchanging local information to maintain team coordination.
Therefore, robots need to consider high-level teammate intentions during action
selection. In this letter, we present the first Macro Action Decentralized
Exploration Network (MADE-Net) using multi-agent deep reinforcement learning
(DRL) to address the challenges of communication dropouts during multi-robot
exploration in unseen, unstructured, and cluttered environments. Simulated
robot team exploration experiments were conducted and compared against
classical and DRL methods where MADE-Net outperformed all benchmark methods in
terms of computation time, total travel distance, number of local interactions
between robots, and exploration rate across various degrees of communication
dropouts. A scalability study in 3D environments showed a decrease in
exploration time with MADE-Net with increasing team and environment sizes. The
experiments presented highlight the effectiveness and robustness of our method.
