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About mql5

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Recent Entries

Population optimization algorithms: Artificial Multi-Social Search Objects (MSO)

by mql5 on Yesterday at 09:21 AM
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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Modified Grid-Hedge EA in MQL5 (Part IV): Optimizing Simple Grid Strategy (I)

by mql5 on 07-02-2024 at 05:58 AM
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.

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Neural networks made easy (Part 40): Using Go-Explore on large amounts of data

by mql5 on 06-29-2024 at 07:22 AM
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.

Neural networks made easy (Part 64): ConserWeightive Behavioral Cloning (CWBC) method

by mql5 on 06-25-2024 at 06:27 AM
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.

Neural networks made easy (Part 63): Unsupervised Pretraining for Decision Transformer (PDT)

by mql5 on 06-21-2024 at 06:27 AM
PDT jointly learns an embedding space of future trajectory as well as a future prior conditioned only on past information.. By conditioning action prediction on the target future embedding, PDT is endowed with the ability to "reason over the future". This ability is naturally task-independent and can be generalized to different task specifications.

To achieve efficient online fine-tuning in downstream tasks, you can easily adapt the framework to new conditions by associating each

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