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AIAA Infotech@Aerospace Conference
The performance of many complex UAV decision-making problems can be extremely sensitive to small errors in the model parameters. One way of mitigating this sensitivity is by designing algorithms that more effectively learn the model throughout the course of a mission. This paper addresses this important problem by considering model uncertainty in a multi-agent Markov Decision Process (MDP) and using an active learning approach to quickly learn transition model parameters. We build on previousdoi:10.2514/6.2009-1981 fatcat:5ijoj6epw5h3jdzujw5ov53ciq