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

Recent advances in decision making have incorporated both risk and ambiguity in decision
theory and optimization methods. These methods implement a variety of uncertainty …

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 …

Robust Markov decision processes (MDPs) compute reliable solutions for dynamic decision
problems with partially-known transition probabilities. Unfortunately, accounting for …

Abstract Robust MDPs (RMDPs) can be used to compute policies with provable worst-case
guarantees in reinforcement learning. The quality and robustness of an RMDP solution are …

We describe two efficient, and exact, algorithms for computing Bellman updates in robust
Markov decision processes (MDPs). The first algorithm uses a homotopy continuation …

Abstract Robust Markov decision processes (RMDPs) are a useful building block of robust
reinforcement learning algorithms but can be hard to solve. This paper proposes a fast …

Despite being a fundamental building block for reinforcement learning, Markov decision
processes (MDPs) often suffer from ambiguity in model parameters. Robust MDPs are …

Making decisions in complex environments is a key challenge in artificial intelligence (AI).
Situations involving multiple decision makers are particularly complex, leading to …

Multi-objective verification problems of parametric Markov decision processes under
optimality criteria can be naturally expressed as nonlinear programs. We observe that many …