Apprenticeship learning via inverse reinforcement learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Active Learning for Reward Estimation in Inverse Reinforcement Learning
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Bayesian inverse reinforcement learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Selecting Operator Queries Using Expected Myopic Gain
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
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We consider semi-autonomous agents that have uncertain knowledge about their environment, but can ask what action the operator would prefer taking in the current or in a potential future state. Asking queries can help improve behavior, but if queries come at a cost (e.g., due to limited operator attention), the number of queries needs to be minimized. We develop a new algorithm for selecting action queries by adapting the recently proposed Expected Myopic Gain (EMG) from its prior use in settings with reward or transition probability queries to our setting of action queries, and empirically compare it to the current state of the art.