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Redirected Walking (RDW) steering algorithms have traditionally relied on human-engineered logic. However, recent advances in reinforcement learning (RL) have produced systems that surpass human performance on a variety of control tasks. This paper investigates the potential of using RL to develop a novel reactive steering algorithm for RDW. Our approach uses RL to train a deep neural network that directly prescribes the rotation, translation, and curvature gains to transform a virtual environment given a user's position and orientatio
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