# CAREER: Scaling Deep Reinforcement Learning for Societal-Scale Cyber-Physical Systems

> **NSF 01002627DB NSF RESEARCH & RELATED ACTIVIT** · Pennsylvania State Univ University Park (PA) · $685,588

## Abstract

This project will develop artificial intelligence techniques to improve the operation of large-scale infrastructure systems, such as smart transportation networks and electric power grids, which are essential to modern life. These complex systems, known as societal-scale cyber-physical systems, integrate physical infrastructure with thousands of sensors, computing devices, and actuators. Due to their scale and distributed nature, managing these systems in real time poses a significant challenge. To address this challenge, the project will leverage deep reinforcement learning, a form of artificial intelligence that learns optimal decision-making strategies directly from data. By improving the efficiency and reliability of critical infrastructure systems, the research will further the national interest through reducing traffic congestion, improving emergency response times, and increasing the stability of the power grid. This project will also develop a highly skilled science, technology, engineering, and mathematics (STEM) talent pipeline. These development activities will include creating interactive, game-based learning modules for K-12 students, developing an interdisciplinary graduate course on AI and cyber-physical systems, and providing training materials to help community partners effectively adopt artificial intelligence technologies.

This project will advance the foundations of sequential decision-making for societal-scale cyber-physical systems (CPS) by addressing the fundamental scalability and communication challenges faced by existing deep reinforcement learning (DRL) approaches. The scope of the research will encompass theoretical and algorithmic innovations to develop a hierarchical and communication-aware DRL framework, which will be tailored to the vast state-action spaces and distributed nature of societal-scale CPS. First, the project will introduce a cyber-physical feudal reinforcement learning framework that will provide automated, data-driven

## Key facts

- **NSF award ID:** 2542543
- **Awardee organization:** Pennsylvania State Univ University Park (PA)
- **SAM.gov UEI:** NPM2J7MSCF61
- **PI:** Aron B Laszka
- **Primary program:** 01002627DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** Artificial Intelligence (AI), CAREER-Faculty Erly Career Dev, CYBER-PHYSICAL SYSTEMS (CPS)
- **Estimated total:** $685,588
- **Funds obligated:** $685,588
- **Transaction type:** Standard Grant
- **Period:** 07/01/2026 → 06/30/2031

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2542543

## Citation

> US National Science Foundation, Award 2542543, CAREER: Scaling Deep Reinforcement Learning for Societal-Scale Cyber-Physical Systems. Retrieved via AI Analytics 2026-07-17 from https://api.ai-analytics.org/grant/nsf/2542543. Licensed CC0.

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