Mastering Uncertainty: An Effective Approach To Training Machines For Real-world Situations

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.