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Recently I've been reading a lot about Q-learning with Neural Networks and thought about to update an existing old optimization system in a power plant boiler composed of a simple feed-forward neural network approximating an output from many sensory inputs. The output then is linked to a linear model-based controller that somehow output again an optimal action so the whole model can converge to the desired goal.

Identifying linear models is a consuming task. I thought about refurbishing the whole thing to model-free Q-learning with a Neural Network approximation of the Q-function. I drew a diagram to ask you if I'm on the right track or not.


My question: if you think I understood well the concept, should my training set be composed of State Features vectors from one side and Q_target - Q_current (here I'm assuming there's an increasing reward) in order to force the whole model towards the target or am I missing something?

Note: The diagram shows a comparison between the old system in the upper part and my proposed change on the lower part.

EDIT: Does a State Neural Network guarantee Experience Replay?

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The training network Q and The target network Q` will be getting updated as we will run more and more episodes during its training.

And for the question” Does a State Neural Network guarantee Experience Replay?”, yes it guarantees the Experience Replay as in the famous DQN(Deep-Q-Network)algorithm, a buffer of past experiences is used to stabilize training by decorrelating the training examples in each batch used to update the neural network.

For a better understanding of the Reinforcement Learning with Hindsight Experience Replay, refer the following link: https://towardsdatascience.com/reinforcement-learning-with-hindsight-experience-replay-1fee5704f2f8

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