Why is my Soft Actor-Critic's policy and value function losses not converging?

Artificial Intelligence Asked by Zahra on December 7, 2020

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?

One Answer

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.

Correct answer by oleg.mosalov on December 7, 2020

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