mql5

The last two articles were devoted to the Soft Actor-Critic algorithm. As you remember, the algorithm is used to train stochastic models in a continuous action space. The main feature of this method is the introduction of an entropy component into the reward function, which allows us to adjust the balance between environmental exploration and model operation. At the same time, this approach imposes some restrictions on the trained models. Using entropy requires some idea of the probability of taking actions, which is quite difficult to directly calculate for a continuous space of actions.

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