Machine learning (ML) models have been widely used to make predictions.

Current approaches to learning cooperative multi-agent behaviors assume relatively restrictive settings.

Modularity is a critical design principle for both software systems and artificial intelligence (AI).

Autonomous agents operating in real-world scenarios frequently encounter uncertainty and make decisions based on incomplete information.

Agent-based models (ABMs) are proliferating as decision-making tools across policy areas in transportation, economics, and epidemiology.