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  1. Neural networks made easy (Part 62): Using Decision Transformer in hierarchical models

    by , Yesterday at 06:28 AM
    Previously, we considered hierarchical models for solving problems with, so to speak, the classical approach of the Markov process. However, the advantages of using hierarchical approaches also apply to sequence analysis problems. One such algorithm is the Control Transformer presented in the article "Control Transformer: Robot Navigation in Unknown Environments through PRM-Guided Return-Conditioned Sequence Modeling". The method authors position it as a new architecture designed to solve
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  2. Master MQL5 from beginner to pro (Part I): Getting started with programming

    by , 06-17-2024 at 05:25 AM
    I sometimes receive private messages from those who want to learn how to create their own Expert Advisors or indicators. Although there is a lot of material on this site and on the Internet in general, including very good resources with examples, beginners still need help. Some users seek more consistency in presentation, others require clarity or something else. Sometimes users ask: "Add comments to the code of a working Expert Advisor, I will understand everything and make the same one myself!"
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  3. News Trading Made Easy (Part 1): Creating a Database

    by , 06-07-2024 at 04:35 AM
    In this article, we will learn to create a database in which we will store data from the MQL5 Economic Calendar. This data can be used later, in upcoming articles, to trade the news. We will also explore how to execute basic SQL queries to retrieve certain organized information from this database. The entire process will be done in the MQL5 IDE.

    Traders keep a close watch on news sources for information that might impact the markets. This includes geopolitical events, corporate
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  4. Neural networks made easy (Part 67): Using past experience to solve new tasks

    by , 06-01-2024 at 02:58 PM
    Reinforcement learning is built on maximizing the reward received from the environment during interaction with it. Obviously, the learning process requires constant interaction with the environment. However, situations are different. When solving some tasks, we can encounter various restrictions on such interaction with the environment. A possible solution for such situations is to use offline reinforcement learning algorithms. They allow you to train models on a limited archive of trajectories
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  5. Neural networks made easy (Part 66): Exploration problems in offline learning

    by , 05-29-2024 at 02:58 PM
    In this article, we will get acquainted with the Exploratory Data for Offline RL (ExORL) framework, which was presented in the paper "Don't Change the Algorithm, Change the Data: Exploratory Data for Offline Reinforcement Learning". The results presented in that article demonstrate that the correct approach to data collection has a significant impact on the final learning outcomes. This impact is comparable to that of the choice of learning algorithm and model architecture.
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