We propose policy gradient algorithms for robust infinite-horizon Markov decision processes
(MDPs) with non-rectangular uncertainty sets, thereby addressing an open challenge in the …

To overcome the sim-to-real gap in reinforcement learning (RL), learned policies must
maintain robustness against environmental uncertainties. While robust RL has been widely …

In offline reinforcement learning (RL), the absence of active exploration calls for attention on
the model robustness to tackle the sim-to-real gap, where the discrepancy between the …

We study the problem of Distributionally Robust Constrained RL (DRC-RL), where the goal
is to maximize the expected reward subject to environmental distribution shifts and …

The sim-to-real gap, which represents the disparity between training and testing
environments, poses a significant challenge in reinforcement learning (RL). A promising …

Robust Markov Decision Processes (RMDPs) have recently been recognized as a valuable
and promising approach to discovering a policy with creditable performance, particularly in …

Robust Markov decision process (robust MDP) is an important machine learning framework
to make a reliable policy that is robust to environmental perturbation. Despite empirical …