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 board games. 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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