Gated Recurrent Units (GRUs) are a type of recurrent neural network architecture designed to capture sequential data and temporal dependencies effectively. GRUs improve upon traditional recurrent neural networks by using gating mechanisms, which help in controlling the flow of information and maintaining long-term dependencies, making them well-suited for tasks involving perception and decision-making in dynamic environments.
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