59 citations · 82 across the 12 of their papers we have counts for
28 papers
COOL-MC: A Comprehensive Tool for Reinforcement Learning and Model Checking
Dennis Gross, Nils Jansen, Sebastian Junges +1
This paper presents COOL-MC, a tool that integrates state-of-the-art reinforcement learning (RL) and model checking. Specifically, the tool builds upon the OpenAI gym and the proba…
Inductive Synthesis of Finite-State Controllers for POMDPs
Roman Andriushchenko, Milan Ceska, Sebastian Junges +1
We present a novel learning framework to obtain finite-state controllers (FSCs) for partially observable Markov decision processes and illustrate its applicability for indefinite-h…
Gradient-Descent for Randomized Controllers under Partial Observability
Linus Heck, Jip Spel, Sebastian Junges +2
Randomization is a powerful technique to create robust controllers, in particular in partially observable settings. The degrees of randomization have a significant impact on the sy…
Convex Optimization for Parameter Synthesis in MDPs
Murat Cubuktepe, Nils Jansen, Sebastian Junges +2
Probabilistic model checking aims to prove whether a Markov decision process (MDP) satisfies a temporal logic specification. The underlying methods rely on an often unrealistic ass…
Model Checking Finite-Horizon Markov Chains with Probabilistic Inference
Steven Holtzen, Sebastian Junges, Marcell Vazquez-Chanlatte +3
We revisit the symbolic verification of Markov chains with respect to finite horizon reachability properties. The prevalent approach iteratively computes step-bounded state reachab…
Model Repair Revamped: On the Automated Synthesis of Markov Chains
Milan Ceska, Christian Dehnert, Nils Jansen +2
This paper outlines two approaches|based on counterexample-guided abstraction refinement (CEGAR) and counterexample-guided inductive synthesis (CEGIS), respectively to the automate…