7 citations · 8 across the 4 of their papers we have counts for
4 papers
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…
Bidding Games on Markov Decision Processes with Quantitative Reachability Objectives
Guy Avni, Martin Kurečka, Kaushik Mallik +2
Graph games are fundamental in strategic reasoning of multi-agent systems and their environments. We study a new family of graph games which combine stochastic environmental uncert…
Threshold UCT: Cost-Constrained Monte Carlo Tree Search with Pareto Curves
Martin Kurečka, Václav Nevyhoštěný, Petr Novotný +1
Constrained Markov decision processes (CMDPs), in which the agent optimizes expected payoffs while keeping the expected cost below a given threshold, are the leading framework for…
Lower bound on the size of a quasirandom forcing set of permutations
Martin Kurecka
A set of permutations is forcing if for any sequence of permutations where the density converges to for every permutati…