in some quality control applications, the quality of a process or a product is described by the relationship between a response variable and one or more explanatory variables, called a profile. moreover, in most practical applications, the qualitative characteristic of a product/service is vague, uncertain and linguistic and cannot be precisely stated. the purpose of this paper is to propose a ...

This paper analyses Rosca's ARL as a general framework for automatic subroutine discovery. We review and compare a number of heuristics for code selection, and experimentally test their e ectiveness in the ARL framework. We also propose and analyse a new heuristic, the Saliency, and two extensions to ARL: di usion of the new subroutines through mutation and the MaxFit technique to adaptively ch...

Computing Bias-Optimal Policies Sridhar Mahadevan Department of Computer Science and Engineering University of South Florida Tampa, Florida 33620 [email protected] Abstract Average-reward reinforcement learning (ARL) is an undiscounted optimality framework that is generally applicable to a broad range of control tasks. ARL computes gain-optimal control policies that maximize the expected pa...

Recently, there has been growing interest in average-reward reinforcement learning (ARL), an undiscounted optimality framework that is applicable to many diierent control tasks. ARL seeks to compute gain-optimal control policies that maximize the expected payoo per step. However, gain-optimality has some intrinsic limitations as an optimality criterion, since for example, it cannot distinguish ...

Average-reward reinforcement learning (ARL) is an undiscounted optimality framework that is generally applicable to a broad range of control tasks. ARL computes gain-optimal control policies that maximize the expected payoff per step. However, gainoptimality has some intrinsic limitations as an optimality criterion, since for example, it cannot distinguish between different policies that all re...

Under estimated in-control parameters, the Phase II control chart performance is expected to vary among practitioners due to the use of different Phase I data sets. Accordingly, the typical measure of Phase II control chart performance, the average run length (ARL), becomes a random variable. In the literature, control charts with estimated parameters were assessed and the appropriate amounts o...

Multiple-input multiple-output (MIMO) radar has become a thriving subject of research during the past decades. In the MIMO radar context, it is sometimes more accurate to model the radar clutter as a nonGaussian process, more specifically, by using the spherically invariant random process (SIRP) model. In this paper, we focus on the estimation and performance analysis of the angular spacing bet...

Primary cilia are sensory organelles indispensable for organogenesis and tissue pattern formation. Ciliopathy small GTPase ARLs are proposed as prominent ciliary switches, which when disrupted result in dysfunctional cilia, yet how ARLs are activated remain elusive. Here, we discover a novel small GTPase functional module, which contains ARL-3, ARL-13, and UNC-119, localizes near the poorly und...

Active Reinforcement Learning (ARL) is a twist on RL where the agent observes reward information only if it pays a cost. This subtle change makes exploration substantially more challenging. Powerful principles in RL like optimism, Thompson sampling, and random exploration do not help with ARL. We relate ARL in tabular environments to BayesAdaptive MDPs. We provide an ARL algorithm using Monte-C...

The incidence of calcium oxalate stone in men is higher than that in women. We evaluated the association between the androgen receptor (AR) and urinary oxalate excretion using Cre-floxed male androgen receptor-knockout (ARKO) mice (ARL−/Y) and floxed mice (ARL+/Y) as control. Fourteen-week-old ARL−/Y and ARL+/Y mice were fed distilled water. Drinking water was then swapped for 0.5% ethylene gly...