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A new algorithm developed at MIT determines whether a machine-learning system should try to mimic its teacher or explore on its own through trial-and-error.
Researchers develop an algorithm that decides when a “student” machine should follow its teacher, and when it should learn on its own. Someone learning to play tennis might hire a teacher to help them learn faster. Because this teacher is (hopefully) a great tennis player, there are times when tr
A new algorithm makes headway in solving the problem of exploration vs. exploitation in reinforcement learning. Developed by MIT CSAIL researchers, it attempts to make decision-making more efficient.