1 citations · 3 across the 7 of their papers we have counts for
5 papers · 1 filter
Missingness-MDPs: Bridging the Theory of Missing Data and POMDPs
Joshua Wendland, Markel Zubia, Roman Andriushchenko +6
We introduce missingness-MDPs (miss-MDPs), a novel subclass of partially observable Markov decision processes (POMDPs) that incorporates the theory of missing data. A miss-MDP is a…
Finite-State Controllers for (Hidden-Model) POMDPs using Deep Reinforcement Learning
David Hudák, Maris F. L. Galesloot, Martin Tappler +3
Solving partially observable Markov decision processes (POMDPs) requires computing policies under imperfect state information. Despite recent advances, the scalability of existing…
Robust Finite-Memory Policy Gradients for Hidden-Model POMDPs
Maris F. L. Galesloot, Roman Andriushchenko, Milan Češka +2
Partially observable Markov decision processes (POMDPs) model specific environments in sequential decision-making under uncertainty. Critically, optimal policies for POMDPs may not…
Pessimistic Iterative Planning with RNNs for Robust POMDPs
Maris F. L. Galesloot, Marnix Suilen, Thiago D. Simão +4
Robust POMDPs extend classical POMDPs to incorporate model uncertainty using so-called uncertainty sets on the transition and observation functions, effectively defining ranges of…
Factored Online Planning in Many-Agent POMDPs
Maris F. L. Galesloot, Thiago D. Simão, Sebastian Junges +1
In centralized multi-agent systems, often modeled as multi-agent partially observable Markov decision processes (MPOMDPs), the action and observation spaces grow exponentially with…