In recent years, the development of robotics and artificial intelligence (AI) systems has been
nothing short of remarkable. As these systems continue to evolve, they are being utilized in …

Sample-efficiency guarantees for offline reinforcement learning (RL) often rely on strong
assumptions on both the function classes (eg, Bellman-completeness) and the data …

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

Offline reinforcement learning (RL) enables learning a decision-making policy without
interaction with the environment. This makes it particularly beneficial in situations where …

Offline safe reinforcement learning (RL) algorithms promise to learn policies that satisfy
safety constraints directly in offline datasets without interacting with the environment. This …

In this work, we decouple the iterative bi-level offline RL (value estimation and policy
extraction) from the offline training phase, forming a non-iterative bi-level paradigm and …

MDPs with low-rank transitions—that is, the transition matrix can be factored into the product
of two matrices, left and right—is a highly representative structure that enables tractable …

Offline reinforcement learning (RL) aims to find an optimal policy for sequential decision-
making using a pre-collected dataset, without further interaction with the environment …

Offline reinforcement learning (RL), which refers to decision-making from a previously-
collected dataset of interactions, has received significant attention over the past years. Much …

Offline goal-conditioned reinforcement learning (GCRL) promises general-purpose skill
learning in the form of reaching diverse goals from purely offline datasets. We propose …