Redirection Controller Using Reinforcement Learning
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In the redirected walking fields, there is a high demand for planning the redirected walking techniques and applying them to physical environments with obstacles. Such techniques are mainly managed using three kinds of methods: direct scripting, generalized controller, and physical- or virtual-environment analysis to determine user redirection. The first approach is effective when a user's path and both physical and virtual environments are fixed; however, it is difficult to handle irregular movements and reuse other environments. The second approach has the potential of reusing any environment but is less optimized. The last approach is the most expected and highly versatile but has still not been developed enough. In this study, we propose a novel redirection controller using reinforcement learning, which has advanced plannability/versatility. Our simulation experiments showed that the proposed strategy can reduce the number of resets by 20.3% for physical-space conditions with multiple obstacles.
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