Project Grant 2621991

The National Science Foundation Division of Electrical, Communications and Cyber Systems awarded The University of Central Florida Board of Trustees $300,461 on October 1, 2026, through the Engineering program (CFDA 47.041) to develop theoretical foundations for long-term reinforcement learning under uncertainty.

The project advances foundational artificial intelligence research by developing decision-making algorithms that optimize for steady-state, long-term performance in autonomous systems. Current reinforcement learning approaches prioritize immediate gains, creating short-sighted behaviors vulnerable to failure in continuous, extended operations. The research addresses this limitation by establishing theoretical and algorithmic foundations for robust average-reward reinforcement learning that maintains reliability over indefinite horizons under model uncertainty. The project specifically targets three primary constraints in existing frameworks: lack of finite-sample complexity guarantees, underdeveloped multi-agent formulations, and restrictive single-recurrent-class assumptions. The work supports safe integration of autonomous systems into safety-critical applications including healthcare, power grid management, and autonomous transportation.

Beyond algorithmic development, the project integrates research with education through open-source software libraries, new curriculum modules, robotics capstone projects, and specialized training for undergraduate and graduate students. Work is performed in Alafaya, Florida, with a period of performance from October 1, 2026, through September 30, 2029. The award is a project grant with $300,461 obligated.