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MIT and Technion researchers have developed an adaptive algorithm that combines imitation and reinforcement learning to optimize machine learning. This algorithm autonomously decides when to follow or diverge from a teacher model, improving training efficiency and effectiveness. The researchers tested the approach in simulations and found that their combination of trial-and-error learning and imitation learning achieved better results and faster learning.
