Multi-Agent Navigation with Reinforcement Learning Enhanced Information Seeking

Multi-agent robotic networks allow simultaneous observations at different positions while avoiding a single point of failure, which is essential for emergency and time-critical applications. Autonomous navigation is vital to the task accomplishment of a multi-agent network in challenging global navigation satellite systems (GNSS)-denied environments. In these environments, agents can rely on inter-agent measurements for self-positioning. In addition, agents can conduct information seeking, i.e., they can proactively adapt their formation to enrich themselves with position information. Classical signal processing tools can efficiently exploit the knowledge of system and measurement models, but are not applicable for long-term objectives. On the other hand, data-driven approaches like reinforcement learning (RL) are suitable for long-term action planning but have to face the critical curse of dimensionality. In this paper, we propose a multi-agent navigation scheme with RL-enhanced information seeking, which simultaneously takes advantage of model-based and data-driven approaches to collaboratively accomplish challenging objectives while exploring a GNSS-denied environment.