moving the docs to github

docs_raw/docs/algorithms/policy_optimization/ac.md

@@ -0,0 +1,27 @@
+# Actor-Critic
+
+**Actions space:** Discrete|Continuous
+
+**References:** [Asynchronous Methods for Deep Reinforcement Learning](https://arxiv.org/abs/1602.01783)
+
+## Network Structure 
+<p style="text-align: center;">
+<img src="..\..\design_imgs\ac.png" width=500>
+</p>
+## Algorithm Description
+
+### Choosing an action - Discrete actions
+
+The policy network is used in order to predict action probabilites. While training, a sample is taken from a categorical distribution assigned with these probabilities. When testing, the action with the highest probability is used.
+
+### Training the network
+A batch of $ T_{max} $ transitions is used, and the advantages are calculated upon it.
+
+Advantages can be calculated by either of the following methods (configured by the selected preset) -
+
+1. **A_VALUE** - Estimating advantage directly:$$ A(s_t, a_t) = \underbrace{\sum_{i=t}^{i=t + k - 1} \gamma^{i-t}r_i +\gamma^{k} V(s_{t+k})}_{Q(s_t, a_t)} - V(s_t) $$where $k$ is $T_{max} - State\_Index$ for each state in the batch.
+2. **GAE** - By following the [Generalized Advantage Estimation](https://arxiv.org/abs/1506.02438) paper. 
+
+The advantages are then used in order to accumulate gradients according to 
+$$ L = -\mathop{\mathbb{E}} [log (\pi) \cdot A] $$
+