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Rewards and punishments in different forms are pervasive and present in awide variety of decision-making scenarios. By observing the outcome of asufficient number of repeated trials, one would gradually learn the value andusefulness of a particular policy or strategy. However, in a given environment,the outcomes resulting from different trials are subject to chance influenceand variations. In learning about the usefulness of a given policy, significantcosts are involved in systematically undertaking the sequential trials;therefore, in most learning episodes, one would wish to keep the cost withinbounds by adopting learning stopping rules. In this paper, we examine thedeployment of different stopping strategies in given learning environmentswhich vary from highly stringent for mission critical operations to highlytolerant for non-mission critical operations, and emphasis is placed on theformer with particular application to aviation safety. In policy evaluation,two sequential phases of learning are identified, and we describe the outcomesvariations using a probabilistic model, with closedform expressions obtainedfor the key measures of performance. Decision rules that map the trialobservations to policy choices are also formulated. In addition, simulationexperiments are performed, which corroborate the validity of the theoreticalresults.

Leveraging Reinforcement Learning Techniques for Effective Policy Adoption and Validation