Collaborative Research: CIF: Medium: Statistical and Algorithmic Foundations of Efficient Reinforcement Learning

点击翻译按钮获取中文摘要

As a data-driven paradigm for sequential decision making in unknown environments, Reinforcement Learning (RL) has received significant interest in recent years owing to its potential ability to solve difficult problems associated with future societal and scientific developments. However, the explosion of both model dimensionality and complexity in current and emerging applications exacerbates the challenge of achieving efficient RL in sample-starved situations, where data collection is expensive, time-consuming, or even high-stake, e.g., in clinical trials, online advertising, and autonomous systems. As a result, understanding and improving the sample and computational efficiencies of RL algorithms, sometimes under additional resource and system-level constraints, are rightly understood as critical to the successful deployment of RL in the future. In this project the PIs are involving students at all levels with diverse backgrounds in Electrical and Computer Engineering, and in Statistics, are developing education modules on RL to enrich the curriculum, and are co-organizing workshops and outreach activities to enable the broader dissemination of the project outcomes.Despite decades-long research efforts, the statistical and computational underpinnings of RL are still far from being well understood, especially when it comes to finite-sample and finite-time issues which are of crucial operational value. This research project is bridging the theory-practice gap of modern algorithmic approaches to RL. It is doing so by (i) characterizing fundamental limits for the sample and computations complexities in various RL settings, (ii) by developing performance guarantees and uncertainty quantification schemes, and (iii) by designing new computationally efficient algorithms that are provably near-optimal in terms of sample complexity in both single-agent and multi-agent settings. The expected outcomes will enable the trustworthy adoption of RL algorithms in sample-starved environments. The complementary expertise of the research team is being leveraged to enrich the statistical and algorithmic foundations of RL through model-, policy-, and value-based approaches. New efficient algorithms that rely on function approximation schemes are being developed in order to address the curse of dimensionality; the resulting techniques are intended to lead to non-asymptotic analysis tools that deal with the complicated statistical dependencies present in RL. This rich research agenda is expected to foster multidisciplinary efforts at the intersection of high-dimensional statistics, non-convex optimization, control theory, information theory, and machine learning.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.