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Researchers from the Singapore University of Technology and Design (SUTD) have successfully applied reinforcement learning to a video game problem. The research team created a new complicated movement design software based on an approach that has proven effective in board games like Chess and Go. The findings could possibly impact robotics and automation, ushering in a new era of movement design. Reinforcement learning is a kind of machine learning in which a computer program learns to make decisions by experimenting with various actions and getting feedback. The research team provided the computer with millions of initial motions to create a reinforcement learning program for movement design.
