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This is not necessarily the only way to do this but it would be the approach I'd take. Assuming your agents position is a vector in $\mathbb{R}^d$, then I would have the network take as input this position vector and pass it through a fully connected layer. I would also take as input the matrix and pass it through a convolutional layer(s) and flatten the ...


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Of course depends on your type of data, but Holt-Winter models can have different degree of complexity and use moving average, trend, and seasonality. This is most useful if the data is not hierarchical, meaning that the time-series are independent from each other. If time-series are relatives of each other then you can also try aggregating them, predict at ...


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Firstly, concatenate only works on identical output shape of the axis. Otherwise, the function will not work. Now, your function output size is (None, 32, 50) and (None, 600, 1). Here, '32' and '600' must be same when you want to concatenate. I would like to suggest some advice based on your problem. You can flatten both of them first and then concatenate. ...


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