How to Perform Gradient Descent for DQN Loss Function
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I'm writing the DQN from scratch, and I'm confused of the procedure of updating the evaluateNet from the gradient descent.
The standard DQN algorithm is to define two networks: . Train with minibatch, and update the with gradient descent step on
I define . When update the , I first make the , and then only update , which guarantee the . Then I update the . If I choose the feedforward train method as '', does [1] update the evalNet correctly via gradient descent?
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