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Neural networks (NNs) are used as approximators in reinforcement learning (RL). To update the policy in RL, the actor network's gradients w.r.t its weights are needed. Since NN doesn't have a mathematical expression to work with, how can its derivatives be calculated?

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  • $\begingroup$ What do you mean Neural Networks does not have a mathmatical expression to work with? Neural Networks is purely math in its nature? $\endgroup$ – SandMan Sep 12 at 11:20
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I think what you mean to ask is how can differentiation occur when there's no obvious neural network function to differentiate?

Don't worry - lots of people get confused about this, because it seems like an obvious hole in the puzzle. As mentioned by @AtillaOzgur, neural networks use partial differentiation through backpropagation.

First, take the output of all the neurons (except the one you're about to differentiate by) as a function:

enter image description here

The above diagram represents the output of one neuron. Do this for every neuron in your network until you have a set. Let's call this set function NN. The output of NN (given all your neuron outputs) is what you'd normally plug into your RL policy.

You then differentiate NN by a single neuron (n) as shown:

$$\frac{\partial NN}{\partial n} = \lim_{h\to0} \left(\frac{NN(\text{all other neuron outputs}, n + h) - NN(\text{all other neuron outputs}, n)}{h} \right)$$

In reality however, it's the partial derivative of the activation function (A) with respect to the output of a single neuron (n):

$$\frac{\partial A}{\partial n}$$

So, depending on your activation function, you just plug in your neuron output to a certain expression and you've found the value by which to update your neural network.

I hope this helps. Deep learning is definitely a field with a learning curve, but places like StackExchange are great resources to help you out.

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  • $\begingroup$ So, when we talk about the derivative of a NN w.r.t a weight, we are actually refering to the change in the output of the NN caused by the change of one neuron( to which that weight is associated). Can you explain a bit more why "In reality however, it's the partial derivative of the activation function (A) with respect to the output of a single neuron (n)"? Thanks. $\endgroup$ – qiang li Sep 12 at 17:16
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    $\begingroup$ @qiangli Sure- differentiating the activation function is a mathematical shortcut to computing the weight of each neuron. Instead of plugging everything into the neural net to iterate over again and again, we can activate the result and get a gradient per-neuron. This is possible through some matrix calculus and algebraic rearranging: it's all available online, but learning the rearrangement probably won't deepen your understanding. $\endgroup$ – mcRobusta Sep 12 at 17:21

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