With advances in technologies, data science techniques, and computing equipment, there
has been rapidly increasing interest in the applications of reinforcement learning (RL) to …

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

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

Abstract Model-based offline reinforcement learning (RL) aims to find highly rewarding
policy, by leveraging a previously collected static dataset and a dynamics model. While the …

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 …

Quantifying uncertainty about a policy's long-term performance is important to solve
sequential decision-making tasks. We study the problem from a model-based Bayesian …

Multi-model Markov decision process (MMDP) is a promising framework for computing
policies that are robust to parameter uncertainty in MDPs. MMDPs aim to find a policy that …

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

In reinforcement learning, robust policies for high-stakes decision-making problems with
limited data are usually computed by optimizing the percentile criterion. The percentile …