We consider model-based reinforcement learning in finite Markov Decision Processes (MDPs), focussing on so-called optimistic strategies. Optimism is usually implemented by carrying out extended value iterations, under a constraint of consistency with the estimated model transition probabilities. In this paper, we strongly argue in favor of using the Kullback-Leibler (KL) divergence for this pur...

recently, farzipoor saen [journal of the operational research society, 60(11), 1575–1582 (2009)] proposed a method based on data envelopment analysis to identify optimistic efficient suppliers in the presence of nondiscretionary factors-imprecise data. this short communication aims at showing a computational error in computing the value of preference intensity parameter in farzipoor saen’s [1] ...

Many techniques continue to be proposed so far to get rid of the noise through digital images with more optimistic method. Each technique has its very own drawbacks. Although Fuzzy Mean Median (FMM) has demonstrated promising results on the available techniques, given it utilizes the top features of data mining to get rid of mixed noise. This data mining method is employed to check which sort o...

Uncertain coalitional game deals with situations in which the transferable payoffs are uncertain variables. The uncertain core has been proposed as the solution of uncertain coalitional game. This paper goes further by presenting two definitions of uncertain Shapley value: expected Shapley value and ̨-optimistic Shapley value. Meanwhile, some characterizations of the uncertain Shapley value are...