Offline reinforcement learning allows the training of models based on data collected from interactions with the environment. This allows a significant reduction of the process of interacting with the environment. Moreover, given the complexity of environmental modeling, we can collect real-time data from multiple research agents and then train the model using this data. At the same time, using a static training dataset significantly reduces the environment information available to us. ...
Whitesnake ~ Is this Love
In our previous article, we laid the foundation by assembling the graphical elements of our MetaQuotes Language 5 (MQL5) graphical user interface (GUI) panel. If you recall, the iteration was a static assembly of GUI elements - a mere snapshot frozen in time, lacking responsiveness. It was static and unyielding. Now, let’s unfreeze that snapshot and infuse it with life. In this eagerly anticipated continuation, we’re taking our panel to the next level. more...
For novice traders, understanding the basic principles of optimization algorithms can be a powerful tool in finding profitable trades and minimizing risks. For seasoned professionals, deep knowledge in this area can open up new horizons and help create sophisticated trading strategies that exceed expectations. more...
In the previous article, we considered the evolution of social groups where they moved freely in the search space. However, here I propose that we change this concept and assume that groups move between sectors, jumping from one to another. All groups have their own centers, which are updated at each iteration of the algorithm. In addition, we introduce the concept of memory both for the group as a whole and for each individual particle in it. Using these changes, our algorithm now allows ...
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