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Found 6 papers, 2 papers with code
More for Less: Safe Policy Improvement With Stronger Performance Guarantees
In an offline reinforcement learning setting, the safe policy improvement (SPI) problem aims to improve the performance of a behavior policy according to which sample data has been generated.
Decision-Making Under Uncertainty: Beyond Probabilities
In this paper, the focus is on the uncertainty that goes beyond this classical interpretation, particularly by employing a clear distinction between aleatoric and epistemic uncertainty.
Safe Policy Improvement for POMDPs via Finite-State Controllers
In our novel approach to the SPI problem for POMDPs, we assume that a finite-state controller (FSC) represents the behavior policy and that finite memory is sufficient to derive optimal policies.
Robust Anytime Learning of Markov Decision Processes
Markov decision processes (MDPs) are formal models commonly used in sequential decision-making.
Balancing Wind and Batteries: Towards Predictive Verification of Smart Grids
We study a smart grid with wind power and battery storage.
Robust Finite-State Controllers for Uncertain POMDPs
(3) We linearize this dual problem and (4) solve the resulting finite linear program to obtain locally optimal solutions to the original problem.
