Reinforcement Learned Multiagent Cooperative Navigation in Hybrid Environment With Relational Graph Learning

Abstract

The multirobot cooperative navigation problem (MCNP) is an essential topic in multiagent control. This article proposes a distributed approach named GAR-CoNav to solve the navigation problem of multiagent to multiple destinations in the face of static and dynamic obstacles. Agents are expected to travel to different destinations without conflicting with each other to achieve maximum efficiency. That is, cooperative navigation in hybrid environment. The velocity obstacle encoding is combined with a graph to build a global representation, which helps the agent capture complex interactions in hybrid environment. GAR-CoNav processes and aggregates environmental features through the graph attention network and has scalability for the changing number of entities in the graph. A novel reward function is developed to train agents to achieve an actual cooperative navigation policy. Extensive simulation experiments demonstrate that GAR-CoNav achieves better performance than state-of-the-art methods.