As a corollary, ǫ -optimal schedulers for reward reachability objectives in uniform CT- MDPs can be obtained in polynomial time using a simple backward greedy algorithm....

This paper solves the problem of computing the maximum and mini- mum probability to reach a set of goal states within a given time bound for locally uniform continuous-time

This paper presents an efficient algorithm to compute the maxi- mum (or minimum) probability to reach a set of goal states within a given time bound in a uniform CTMDP, i.e., a CTMDP

Adaptive aggregation for reinforcement learning in average reward markov decision processes. Continuous

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This is based on a novel algorithm using Gaussian Processes (GPs) and statistical model checking to sample in an unbiased manner the gradient of the functional associating

Finally, for the constrained models with the total expected cost, we present in Section 4 the sufficient class of realizable strategies, that is, Poisson-related strategies, and show

Keywords Model Checking, Stochastic Petri nets, Rewards, Continuous-time Markov Chains, Markov Reward Models, Interval Decision Diagrams, Multi-threading, Con- tinuous Stochastic

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The model we present is consistent with the second explanation. The negative shift in labor supply increases the relative abundance of capital and generates an incentive for

Semi-Markov decision processes pro- vide a more permissive model than continuous-time Markov decision processes, since they allow as residence-time distributions any

By defining pseudo-variance, the deviation of the deterministic stationary policy f and the Poisson equation, a series of con- cepts and theorems, we prove the existence of

Learning optimal policies via stochastic functional gradient ascent In this section we give a variational formulation of the control problem of determining the optimal scheduler

The present paper considers the constrained optimal control problem with total undiscounted criteria for a continuous-time Markov decision process (CTMDP) in Borel state and

In this paper, we are interested in the synthesis of schedulers in double-weighted Markov decision processes, which satisfy both a percentile constraint over a weighted

– Under a weak condition, e.g., in the discounted case, for any control strat- egy, there is an equivalent Markov randomized strategy (Theorem 1) and an equivalent mixture of

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