All about MQL5 Wizard : create robots without programming. - page 7

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MQL5 Wizard Techniques you should know (Part 41): Deep-Q-Networks

MQL5 Wizard Techniques you should know (Part 41): Deep-Q-Networks

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.
MQL5 Wizard Techniques you should know (Part 41): Deep-Q-Networks
MQL5 Wizard Techniques you should know (Part 41): Deep-Q-Networks
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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.
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