The goal of robust reinforcement learning (RL) is to learn a policy that is robust against the
uncertainty in model parameters. Parameter uncertainty commonly occurs in many real …

This paper develops the first policy gradient method with global optimality guarantee and
complexity analysis for robust reinforcement learning under model mismatch. Robust …

This paper investigates model robustness in reinforcement learning (RL) via the framework
of distributionally robust Markov decision processes (RMDPs). Despite recent efforts, the …

This paper concerns the central issues of model robustness and sample efficiency in offline
reinforcement learning (RL), which aims to learn to perform decision making from history …

Robustness has been extensively studied in reinforcement learning (RL) to handle various
forms of uncertainty such as random perturbations, rare events, and malicious attacks. In this …

Toward theoretical understandings of robust Markov decision processes: Sample
complexity and asymptotics Page 1 The Annals of Statistics 2022, Vol. 50, No. 6, 3223–3248 …

Abstract Robust Markov decision processes (MDPs) aim to handle changing or partially
known system dynamics. To solve them, one typically resorts to robust optimization methods …

Markov decision processes (MDPs) often suffer from the sensitivity issue under model
ambiguity. In recent years, robust MDPs have emerged as an effective framework to …

Abstract Robust Markov decision processes (RMDPs) provide a promising framework for
computing reliable policies in the face of model errors. Many successful reinforcement …

This paper addresses the problem of model-free reinforcement learning for Robust Markov
Decision Process (RMDP) with large state spaces. The goal of the RMDPs framework is to …