1 citations · 1 across the 1 of their papers we have counts for
3 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 KureÄka, 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 KureÄka, Václav NevyhoÅ¡tÄný, 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…