Researchers train AI with reinforcement learning to defeat champion Street Fighter players
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.
To advance Polar code design for 6G applications, we develop a reinforcement learning-based universal sequence design framework that is extensible and adaptable to diverse channel conditions and decoding strategies. Crucially, our method scales to code lengths up to 2048, making it suitable for use in standardization. Across all (N,K)(N, K)(N,K)…
Researchers at the Hybrid Robotics Group at UC Berkeley, Simon Fraser University and Georgia Institute of Technology have recently created a reinforcement learning model that allows a quadrupedal robot to efficiently play soccer in the role of goalkeeper. The model introduced in a paper pre-published on arXiv, improves the robot's…
An international group of researchers has created a new approach to imitating human motion by combining central pattern generators (CPGs) and deep reinforcement learning (DRL). The method not only imitates walking and running motions but also generates movements for frequencies where motion data is absent, enables smooth transition movements from…