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Found 4 papers, 0 papers with code
Reinforcement Learning for Omega-Regular Specifications on Continuous-Time MDP
We consider CTMDP environments against the learning objectives expressed as omega-regular languages.
PAC Statistical Model Checking of Mean Payoff in Discrete- and Continuous-Time MDP
We provide the first algorithm to compute mean payoff probably approximately correctly in unknown MDP; further, we extend it to unknown CTMDP.
Safe Learning for Near Optimal Scheduling
In this paper, we investigate the combination of synthesis, model-based learning, and online sampling techniques to obtain safe and near-optimal schedulers for a preemptible task scheduling problem.
Mixing Probabilistic and non-Probabilistic Objectives in Markov Decision Processes
In this paper, we consider algorithms to decide the existence of strategies in MDPs for Boolean combinations of objectives.
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