Abstract Markov decision process (MDP) offers a general framework for modelling sequential making where outcomes are random. In particular, it serves as mathematical reinforcement learning. This paper introduces an extension of MDP, namely quantum MDP (qMDP), that can serve model about systems. We develop dynamic programming algorithms policy evaluation and finding optimal policies qMDPs in the...

Warranty is an important factor of marketing products; however, warranty always involves additional costs to the seller and these costs usually depend on product reliability. Burn-in is considered as a part of the production process in which the manufactured products are operated under accelerated stresses for a short time period before their release and has been applied as a way for enhancing ...

A decision maker records measurements of a finite-state Markov chain corrupted by noise. The goal is to decide when the Markov chain hits a specific target state. The decision maker can choose from a finite set of sampling intervals to pick the next time to look at the Markov chain. The aim is to optimize an objective comprising of false alarm, delay cost and cumulative measurement sampling cos...

Abstract: In recent decades, high profits of extended warranty have caused that third-party firms consider it as a lucrative after-sales service. However, customers division in terms of risk aversion and effect of offering extended warranty on manufacturers’ basic warranty should be investigated through adjusting such services. Since risk-averse customers welcome extended warranty, while the cu...

We introduce a new approach to distribution fitting, called Decision on Beliefs (DOB). The objective is to identify the probability distribution function (PDF) of a random variable X with the greatest possible confidence. It is known that f X is a member of = { , , }. 1 m S f L f To reach this goal and select X f from this set, we utilize stochastic dynamic programming and formulate this proble...

c. J. C. H. Watkins 25B Framfield Highbury, London N51UU Decision making tasks that involve delayed consequences are very common yet difficult to address with supervised learning methods. If there is an accurate model of the underlying dynamical system, then these tasks can be formulated as sequential decision problems and solved by Dynamic Programming. This paper discusses reinforcement learni...

in this paper we first describe the stochastic optimal control algorithm called ((optcon)). the algorithm minimizes an intertemporal objective loss function subject to a nonlinear dynamic system in order to achieve optimal value of control (or instrument) variables. second as an application, we implemented the algorithm by the statistical programming system ((gauss)) to determine the optimal fi...

In this paper, we study collaborative filters that adapt future recommendations based on feedback (like/dislike) received for past recommendations. We consider a mathematical model where users and items are clustered, and ratings are noisy. We apply sequential decision techniques to study structure of collaborative filters that maximize the time average of the expected ratings. While our previo...