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Offline reinforcement learning allows the training of models based on data collected from interactions with the environment. This allows a significant reduction of the process of interacting with the environment. Moreover, given the complexity of environmental modeling, we can collect real-time data from multiple research agents and then train the model using this data.

At the same time, using a static training dataset significantly reduces the environment information available to us. Due to the limited resources, we cannot preserve the entire diversity of the environment in the training dataset.

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