Discussing the article: "Neural networks made easy (Part 50): Soft Actor-Critic (model optimization)"

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Check out the new article: Neural networks made easy (Part 50): Soft Actor-Critic (model optimization).

In the previous article, we implemented the Soft Actor-Critic algorithm, but were unable to train a profitable model. Here we will optimize the previously created model to obtain the desired results.

We continue to study the Soft Actor-Critic algorithm. In the previous article, we implemented the algorithm but were unable to train a profitable model. Today we will consider possible solutions. A similar question has already been raised in the article "Model procrastination, reasons and solutions". I propose to expand our knowledge in this area and consider new approaches using our Soft Actor-Critic model as an example.

Before we move directly to optimizing the model we built, let me remind you that Soft Actor-Critic is a reinforcement learning algorithm for stochastic models in a continuous action space. The main feature of this method is the introduction of an entropy component into the reward function.

Using a stochastic Actor policy allows the model to be more flexible and capable of solving problems in complex environments where some actions may be uncertain or incapable of defining clear rules. This policy is often more robust when dealing with data containing a lot of noise since it takes into account the probabilistic component and is not tied to clear rules.

Author: Dmitriy Gizlyk