Collaborative Research: CIF: Medium: MoDL:Toward a Mathematical Foundation of Deep Reinforcement Learning

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Deep Reinforcement Learning (DRL), which uses neural networks to solve sequential decision-making problems, has made breakthroughs in real-world applications, such as robotics, gaming, healthcare, and transportation systems. However, current theoretical work on reinforcement learning is restricted to problems with a small number of states; as these results do not cover neural networks, they cannot be used to satisfactorily explain the empirical successes of DRL. This project seeks to bridge this gap by building a mathematical foundation for DRL that leverages ideas from approximation theory, control theory, and optimization theory. This will allow the computational and statistical complexity of DRL to be systematically characterized, and will help with designing more efficient and reliable empirical methods. Education and outreach plans are integrated into this project. Specifically, the investigators will mentor graduate and undergraduate students (some through the STARS program for underrepresented groups at the University of washington), develop new courses and monographs, organize research workshops, and develop course materials for a high school data science and artificial intelligence curriculum. This project has three major components. The first thrust identifies which types of guarantees are achievable by policies for different reinforcement learning problem instances. Concretely, this requires investigating how increasingly structured problem instances enable stronger guarantees for policies; this will be done by using, and further developing, tools from non-convex optimization to describe policies that achieve stationary points, local maxima, and global maxima of the reward function. The second thrust takes the perspective of approximation theory and capacity control to investigate how the neural network complexity can be gradually increased to eventually find the most complex sub-family of neural networks that permit sample-efficient algorithms. The third thrust builds upon the knowledge gained in the first two thrusts, and is devoted to the design of computationally efficient algorithms; this will be done by leveraging tools from optimization theory and by making connections with control theory.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.