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Principal Component Analysis (PCA) is the focusing on only the ‘principal components’ among the many dimensions of a data set, such that one is reducing the dimensions of that data set by ignoring the ‘non-principal’ parts. PCA though, with eigen values & vectors, take on a slightly deeper approach. Typically, data sets that are handled under PCA are in a matrix format and the principal components, that are sought from a matrix would be a single vector column (or row) that ...
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 ...
In this installment of our ongoing series on the Modified Grid-Hedge EA in MQL5, we delve into the intricacies of the Grid EA. Building on our experience with the Simple Hedge EA, we now apply similar techniques to improve the performance of the Grid EA. Our journey begins with an existing Grid EA, which serves as our canvas for mathematical exploration. The goal? To dissect the underlying strategy, unravel its intricacies, and uncover the theoretical underpinnings that drive its behavior. ...
In the previous article "Neural networks made easy (Part 39): Go-Explore, a different approach to exploration", we familiarized ourselves with the Go-Explore algorithm and its ability to explore the environment. In this article, we will take a closer look at possible optimization methods for the Go-Explore algorithm to improve its efficiency over longer training periods. more...
The Decision Transformer and all its modifications, which we discussed in recent articles, belong to the methods of Behavior Cloning (BC). We train models to repeat actions from "expert" trajectories depending on the state of the environment and the target outcomes. Thus, we teach the model to imitate the behavior of an expert in the current state of the environment in order to achieve the target. more...
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