Technical Note: \cal Q-Learning
Machine Learning
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If robots learn new actions through human-robot interaction, it is important that the robots can utilize rewards as well as instructions to reduce humans' efforts. Additioanlly, "interval" which allows humans to give instructions and evaluations is also important. We hence focused on "delays in initiating actions" and changed them according to the progress of learning: long delays at early stages, and short at later stages. We compared the proposed varying delay with a constant delay by an experiment. The result demonstrated that the varying delay improves learning efficiency significantly and impresses humans as teachable.