Discussing the article: "MQL5 Wizard Techniques you should know (Part 41): Deep-Q-Networks"
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Check out the new article: MQL5 Wizard Techniques you should know (Part 41): Deep-Q-Networks.
The Deep-Q-Network is a reinforcement learning algorithm that engages neural networks in projecting the next Q-value and ideal action during the training process of a machine learning module. We have already considered an alternative reinforcement learning algorithm, Q-Learning. This article therefore presents another example of how an MLP trained with reinforcement learning, can be used within a custom signal class.
Deep-Q-Networks (DQN) are another reinforcement learning algorithm, besides Q-Learning that we looked at in this article, but they, unlike Q-Learning, use neural networks to forecast the q-value and the next action to be taken by the agent. It is similar/ related to Q-Learning in that a Q-Table is still involved, where the cumulative knowledge on actions and states from previous ‘episodes’ gets stored. In fact, it shares the same Wikipedia page as Q-Learning as can be seen from the links where it's defined essentially as a variant of Q-Learning.
The signal class together with the trailing stop class and the money management class are the three main modules that need to be defined when building a wizard assembled Expert Advisor. Putting them together via the MQL5 wizard can be done by following guides that are here and here for new readers. The source code attached to the bottom of this article is meant to be used by following the wizard assembly guides as shared in these links. We are looking, once again, at defining a custom signal class for use in a wizard assembled Expert Advisor.
This is not the only way, though, that we can examine DQN, as implementations for a custom trailing class or a custom money management class can also be made and tested out. We are focusing on the signal class though because determining of the long and short conditions in these Expert Advisors is critical and, in many cases, best demonstrates the potential of a trade setup. This article builds on previous articles in these series, where we dwell on techniques or different setups that can be used in developing customized wizard assembled Expert Advisors and so a review of past articles, for new readers would be a good idea, especially if they are looking to diversify their approach. These articles cover not just a variety of custom signals but custom implementations of the trailing class and the money management class.
Author: Stephen Njuki