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 ...
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 ...
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!" ...
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 ...
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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