  Multi-agent safe systems have become an increasingly important area of study
as we can now easily have multiple AI-powered systems operating together. In
such settings, we need to ensure the safety of not only each individual agent,
but also the overall system. In this paper, we introduce a novel multi-agent
safe learning algorithm that enables decentralized safe navigation when there
are multiple different agents in the environment. This algorithm makes mild
assumptions about other agents and is trained in a decentralized fashion, i.e.
with very little prior knowledge about other agents' policies. Experiments show
our algorithm performs well with the robots running other algorithms when
optimizing various objectives.
