4 papers
Data-Driven Synthesis of Probabilistic Controlled Invariant Sets for Linear MDPs
Kazumune Hashimoto, Shunki Kimura, Kazunobu Serizawa +3
We study data-driven computation of probabilistic controlled invariant sets (PCIS) for safety-critical reinforcement learning under unknown dynamics. Assuming a linear MDP model, w…
Quadratic Truncated Random Return in Distributional LQR: Positive Definiteness, Density, and Log-Concavity
Ruyi Teng, Dan Wang, Wei Chen +1
Distributional linear quadratic regulator (LQR) is a new framework that integrates the distributional reinforcement learning and classical LQR, which offers a new way to study the…
Distributionally Robust Equilibria over the Wasserstein Distance for Generalized Nash Game
Yixun Wen, Yulong Gao, Boli Chen
Generalized Nash equilibrium problem (GNEP) is fundamental for practical applications where multiple self-interested agents work together to make optimal decisions. In this work, w…
Data-Driven Adjustable Robust Optimization
Xiaoxing Ren, Alessio Moreschini, Zhongda Chu +2
In this paper, we develop a two-stage data-driven approach to address the adjustable robust optimization problem, where the uncertainty set is adjustable to manage infeasibility ca…