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Sample complexity of robust reinforcement learning with a generative model
Abstract The Robust Markov Decision Process (RMDP) framework focuses on designing
control policies that are robust against the parameter uncertainties due to the mismatches …
control policies that are robust against the parameter uncertainties due to the mismatches …
Toward theoretical understandings of robust markov decision processes: Sample complexity and asymptotics
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 …
complexity and asymptotics Page 1 The Annals of Statistics 2022, Vol. 50, No. 6, 3223–3248 …
Offline reinforcement learning as anti-exploration
Abstract Offline Reinforcement Learning (RL) aims at learning an optimal control from a fixed
dataset, without interactions with the system. An agent in this setting should avoid selecting …
dataset, without interactions with the system. An agent in this setting should avoid selecting …
Safe policy improvement by minimizing robust baseline regret
An important problem in sequential decision-making under uncertainty is to use limited data
to compute a safe policy, ie, a policy that is guaranteed to perform at least as well as a given …
to compute a safe policy, ie, a policy that is guaranteed to perform at least as well as a given …
Policy gradient in robust mdps with global convergence guarantee
Abstract Robust Markov decision processes (RMDPs) provide a promising framework for
computing reliable policies in the face of model errors. Many successful reinforcement …
computing reliable policies in the face of model errors. Many successful reinforcement …
Fast bellman updates for wasserstein distributionally robust mdps
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 …
ambiguity. In recent years, robust MDPs have emerged as an effective framework to …
Partial policy iteration for l1-robust markov decision processes
Robust Markov decision processes (MDPs) compute reliable solutions for dynamic decision
problems with partially-known transition probabilities. Unfortunately, accounting for …
problems with partially-known transition probabilities. Unfortunately, accounting for …
Confounding-robust policy evaluation in infinite-horizon reinforcement learning
Off-policy evaluation of sequential decision policies from observational data is necessary in
applications of batch reinforcement learning such as education and healthcare. In such …
applications of batch reinforcement learning such as education and healthcare. In such …
Robust -Divergence MDPs
In recent years, robust Markov decision processes (MDPs) have emerged as a prominent
modeling framework for dynamic decision problems affected by uncertainty. In contrast to …
modeling framework for dynamic decision problems affected by uncertainty. In contrast to …
Beyond confidence regions: Tight bayesian ambiguity sets for robust mdps
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 …
guarantees in reinforcement learning. The quality and robustness of an RMDP solution are …