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I'm trying to implement a soft actor-critic algorithm for financial data (stock prices), but I have trouble with losses: no matter what combination of hyper-parameters I enter, they are not converging, and basically it caused bad reward return as well. It sounds like the agent is not learning at all.

I already tried to tune some hyperparameters (learning rate for each network + number of hidden layers), but I always get similar results. The two plots below represent the losses of my policy and one of the value functions during the last episode of training.

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My question is, would it be related to the data itself (nature of data) or is it something related to the logic of the code?

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I would say it is the nature of data. Generally speaking, you are trying to predict a random sequence, especially if you use the history data as an input and try to get the future value as an output.

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  • $\begingroup$ Yes , I am trying another approach now by extracting more data from the temporal series ( historical prices ) by augmenting the data itself, it shows better results but still unstable. $\endgroup$
    – Zahra
    Aug 4, 2020 at 12:05

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