  In the real world, people/entities usually find matches independently and
autonomously, such as finding jobs, partners, roommates, etc. It is possible
that this search for matches starts with no initial knowledge of the
environment. We propose the use of a multi-agent reinforcement learning (MARL)
paradigm for a spatially formulated decentralized two-sided matching market
with independent and autonomous agents. Having autonomous agents acting
independently makes our environment very dynamic and uncertain. Moreover,
agents lack the knowledge of preferences of other agents and have to explore
the environment and interact with other agents to discover their own
preferences through noisy rewards. We think such a setting better approximates
the real world and we study the usefulness of our MARL approach for it. Along
with conventional stable matching case where agents have strictly ordered
preferences, we check the applicability of our approach for stable matching
with incomplete lists and ties. We investigate our results for stability, level
of instability (for unstable results), and fairness. Our MARL approach mostly
yields stable and fair outcomes.
