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Recently, deep reinforcement learning-based methods have been actively studied and have outperformed traditional rule-based approaches owing to their optimization capabilities. Among these methods, those that assume continuous action spaces typically rely on Gaussian distributions, which limit the flexibility of the generated actions. In contrast, the application of diffusion models to reinforcement learning has advanced, enabling more flexible action distributions than Gaussian policy-based approaches. In this study, we apply a diffusion-based reinforcement learning approach to social navigation and validate its effectiveness. 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