moving the docs to github

docs_raw/docs/algorithms/value_optimization/naf.md

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+# Normalized Advantage Functions
+
+**Actions space:** Continuous
+
+**References:** [Continuous Deep Q-Learning with Model-based Acceleration](https://arxiv.org/abs/1603.00748.pdf)
+
+## Network Structure
+
+<p style="text-align: center;">
+
+<img src="..\..\design_imgs\naf.png" width=600>
+
+</p>
+
+## Algorithm Description
+### Choosing an action
+The current state is used as an input to the network. The action mean $ \mu(s_t ) $ is extracted from the output head. It is then passed to the exploration policy which adds noise in order to encourage exploration.
+###Training the network
+The network is trained by using the following targets:
+$$ y_t=r(s_t,a_t )+\gamma\cdot V(s_{t+1}) $$
+Use the next states as the inputs to the target network and extract the $ V $ value, from within the head, to get $ V(s_{t+1} ) $. Then, update the online network using the current states and actions as inputs, and $ y_t $ as the targets.
+After every training step, use a soft update in order to copy the weights from the online network to the target network.