MIT Innovates AI Training for Enhanced Reliability

MIT researchers have developed a more efficient algorithm for training AI agents using reinforcement learning, which strategically selects tasks to improve overall performance while reducing training costs. This method, called Model-Based Transfer Learning (MBTL), enhances the reliability of AI systems in complex tasks like traffic control by focusing on key tasks that maximize performance. The approach is significantly more efficient than traditional methods, offering a 5 to 50 times improvement in training efficiency, and holds potential for application in real-world mobility systems.