3 citations · 7 across the 6 of their papers we have counts for
8 papers · 1 filter
Reward Shaping for Reinforcement Learning with Omega-Regular Objectives
E. M. Hahn, M. Perez, S. Schewe +3
Recently, successful approaches have been made to exploit good-for-MDPs automata (Büchi automata with a restricted form of nondeterminism) for model free reinforcement learning, a…
Symblicit Exploration and Elimination for Probabilistic Model Checking
Ernst Moritz Hahn, Arnd Hartmanns
Binary decision diagrams can compactly represent vast sets of states, mitigating the state space explosion problem in model checking. Probabilistic systems, however, require multi-…
Omega-Regular Objectives in Model-Free Reinforcement Learning
Ernst Moritz Hahn, Mateo Perez, Sven Schewe +3
We provide the first solution for model-free reinforcement learning of ω-regular objectives for Markov decision processes (MDPs). We present a constructive reduction from the almos…
Accelerated Model Checking of Parametric Markov Chains
Paul Gainer, Ernst Moritz Hahn, Sven Schewe
Parametric Markov chains occur quite naturally in various applications: they can be used for a conservative analysis of probabilistic systems (no matter how the parameter is chosen…
Incremental Verification of Parametric and Reconfigurable Markov Chains
Paul Gainer, Ernst Moritz Hahn, Sven Schewe
The analysis of parametrised systems is a growing field in verification, but the analysis of parametrised probabilistic systems is still in its infancy. This is partly because it i…
Synthesising Strategy Improvement and Recursive Algorithms for Solving 2.5 Player Parity Games
Ernst Moritz Hahn, Sven Schewe, Andrea Turrini +1
2.5 player parity games combine the challenges posed by 2.5 player reachability games and the qualitative analysis of parity games. These two types of problems are best approached…