Safe Policy Synthesis in Multi-Agent POMDPs via Discrete-Time Barrier Functions

Understand

A multi-agent partially observable Markov decision process (MPOMDP) is a modeling paradigm used for high-level planning of heterogeneous autonomous agents subject to uncertainty and partial observation.

• Despite their modeling efficiency, MPOMDPs have not received significant attention in safety-critical settings.
• In this paper, we use barrier functions to design policies for MPOMDPs that ensure safety.
• Notably, our method does not rely on discretization of the belief space, or finite memory.