mql5

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 complex control and navigation problems based on reinforcement learning. This method combines modern methods of reinforcement learning, planning and machine learning, which allows us to create adaptive control strategies in a variety of environments.

Control Transformer opens new perspectives for solving complex control problems in robotics, autonomous driving and other fields. I propose to look at the prospects for using this method in solving our trading problems.

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