About the project
This project explores the integration of calculus of variations with verification-guided representation learning for reinforcement learning.
We aim to develop optimal, physically consistent representations that enhance sample efficiency, stabilize learning, and facilitate formal verification. This research addresses critical needs in safe and reliable AI, with applications in robotics, autonomous systems, and beyond.
As reinforcement learning (RL) applications expand into safety-critical domains, the need for models that are both high-performing and verifiable has become paramount.
This project proposes a novel approach to representation learning that combines calculus of variations with verification-guided learning to address this challenge. By leveraging the calculus of variations, we aim to optimize the representations learned by RL agents to be not only effective but also compliant with underlying physical laws and constraints, thereby enhancing sample efficiency and stability in learning. This principled approach ensures that representations capture the essential dynamics of the environment, allowing RL agents to generalize better with less data and to learn more robust policies.
Additionally, the project incorporates formal verification techniques into the representation learning process, guiding the agent to create representations that are inherently verifiable. This ensures that the resulting policies are safer and more reliable, which is critical in applications such as autonomous vehicles, robotics, and industrial control. Students working on this project will explore cutting-edge intersections between machine learning, optimization, and formal methods, developing a framework that addresses both performance and verifiability.
Potential research directions include multi-dimensional calculus of variations for high-dimensional state spaces, efficient integration of physical laws as constraints in representation learning, and the application of verification feedback to refine learned representations.
This project offers PhD students the opportunity to contribute to foundational advancements in RL and to push the boundaries of safe, efficient, and explainable AI.
