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Oct 6, 2023

Researchers train AI with reinforcement learning to defeat champion Street Fighter players

Posted by in categories: robotics/AI, transportation

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. In a single testing, the movements from the new approach appeared to be superior to those of top human players.

These findings could possibly impact robotics and automation, ushering in a new era of movement design. The team’s article in Advanced Intelligence Systems is titled “A Phase-Change Memristive Reinforcement Learning for Rapidly Outperforming Champion Street Fighter Players.”

“Our findings demonstrate that reinforcement learning can do more than just master simple . The program excelled in creating more complex movements when trained to address long-standing challenges in movement science,” said principal investigator Desmond Loke, Associate Professor, SUTD.

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