When modeling real-world decision-theoretic planning problems in the Markov Decision Process (MDP) framework, it is often impossible to obtain a completely accurate estimate of transition probabilities. For example, natural ...

This paper studies the asymptotic optimality of discrete-time Markov decision processes (MDPs) with general state space and action space and having weak and strong interactions. By using a similar approach as developed by ...

This paper presents a dynamic model approach to analyze the utility generated by a customer’s buying behavior dynamics. The dynamic of the model is represented by a class of controllable finite Markov Decision Process ...

© 2017, Springer Science+Business Media New York.Non-stationary domains, where unforeseen changes happen, present a challenge for agents to find an optimal policy for a sequential decision making problem. This work ...

Abstract. This paper proposes a novel and practical model-based learning approach with iterative refinement for solving continuous (and hybrid) Markov decision processes. Initially, an approximate model is learned using ...

Markov decision processes (MDP) are useful to model optimisation problems in concurrent systems. To verify MDPs with efficient Monte Carlo techniques requires that their nondeterminism be resolved by a scheduler. Recent ...