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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.

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We continue the development of multi-symbol, multi-period indicators which we started in the previous article.

A single-color indicator buffer is a regular double array, which is filled with data when calculating the indicator. We can obtain data from this array and display it on a chart using the CopyBuffer() function provided that the receiving array will be a double array set as an indicator's plotting buffer (SetIndexBuffer()). When copying data from the buffer of the calculated

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