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

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 …

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 …

Preference-based feedback is important for many applications in reinforcement learning
where direct evaluation of a reward function is not feasible. A notable recent example arises …

A significant roadblock to the development of principled multi-agent reinforcement learning
is the fact that desired solution concepts like Nash equilibria may be intractable to compute …

We introduce a novel method for safe mobile robot navigation in dynamic, unknown
environments, utilizing onboard sensing to impose safety constraints without the need for …

This paper presents a novel Wasserstein distributionally robust control and state estimation
algorithm for partially observable linear stochastic systems, where the probability …

In many practical applications of reinforcement learning (RL), it is expensive to observe state
transitions from the environment. In the problem of plasma control for nuclear fusion, the …