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The creation of negative rewards leads to the chance of Q-values being negative. However, networks with relu or sigmoid activations, just cannot predict negative values. This will lead to a case where erroneous Q-values are being predicted. Is my understanding correct?

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A network with ReLU activation can predict negative values; we put ReLU between the hidden layers but return the output of the final layer without any activation function, or with a linear activation function to scale the output.

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    $\begingroup$ It is worth mentioning that some kind of non-linear activation function is necessary for hidden layers to be useful. Only in the output layer should be made linear in most cases (I hesitate to say all because although I cannot think of any reason, someone will probably know of a case where it is useful to have a linear hidden layer). $\endgroup$ Jun 10 at 7:17

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