  This paper addresses the considerations that comes along with adopting
decentralized communication for multi-agent localization applications in
discrete state spaces. In this framework, we extend the original formulation of
the Bayes filter, a foundational probabilistic tool for discrete state
estimation, by appending a step of greedy belief sharing as a method to
propagate information and improve local estimates' posteriors. We apply our
work in a model-based multi-agent grid-world setting, where each agent
maintains a belief distribution for every agents' state. Our results affirm the
utility of our proposed extensions for decentralized collaborative tasks. The
code base for this work is available in the following repo
