Learning Decentralized Multi-Robot PointGoal Navigation

Takieddine Soualhi, Nathan Crombez, Yassine Ruichek, Alexandre Lombard, Stéphane Galland

Abstract: Integrating robots into real-world applications requires effective consideration of various agents, including other robots. Multi-agent reinforcement learning (MARL) is an established field that addresses multi-agent systems problems by leveraging reinforcement learning techniques. Despite its potential, the study of multi-robot systems, particularly in vision-based robotics, remains in its early stages. In this context, this article tackles the PointGoal navigation problem for multi-robot systems, extending the traditional single agent focus to a multi-agent context. To this end, we introduce a training environment designed to address vision-based multi-robot challenges. In addition, we propose a method based on the centralized training-decentralized execution paradigm within MARL to explore three PointGoal navigation scenarios: the SpecificGoal scenario, where each agent has a distinct target; the CommonGoal scenario, where all agents share the same target; and the Ad-hoCoop scenario, which requires agents to adapt to varying team sizes. Our results contribute to lay the groundwork for adopting MARL approaches to address vision-based tasks for multi-robot systems.