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MIT researchers develop an efficient way to train more reliable AI agents
MIT researchers developed an efficient approach for training more reliable reinforcement learning models, focusing on complex tasks that involve variability. This could enable the leverage of reinforcement learning across a wide range of applications.
Fields ranging from robotics to medicine to political science are attempting to train AI systems to make meaningful decisions of all kinds. For example, using an AI system to intelligently control traffic in a congested city could help motorists reach their destinations faster, while improving safety or sustainability. An algorithm that is not very complicated stands a better chance of being adopted by the community because it is easier to implement and easier for others to understand,” says senior author Cathy Wu, the Thomas D. and Virginia W. Cabot Career Development Associate Professor in Civil and Environmental Engineering (CEE) and the Institute for Data, Systems, and Society (IDSS), and a member of the Laboratory for Information and Decision Systems (LIDS).
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