Update IntelligentPoolDetails.md

Author: tcmxx

Documents/IntelligentPoolDetails.md

@@ -10,6 +10,9 @@ The general name of those types of those games is cue sport(See [Wikipedia](http
         alt="BilliardGame" 
         width="600" border="10" />
 </p>
+<p align="center">
+  <em>Image from Google()</em>
+</p>
 
 During the development of the materials for the Computational Intelligence in Games course, we decided to develop a whole set of examples with the billiard game, using different technologies, to showcase the concepts and power of each. The start of the examples is a simple case where the AI only need to hit the white ball once and try to make both of the red balls on desk into pockets, using [MAES(CMAES)](https://en.wikipedia.org/wiki/CMA-ES) algorithm. The final goal is to develop a AI that can play a whole game with itself, where it should plan not just one shot but multiple shots and prevent the opponents from getting advantages, using [PPO](https://arxiv.org/abs/1707.06347). 
 
@@ -159,3 +162,14 @@ If we use the output from neural network as the initial guess of the optimizer,
 ### Case 6 - 1 red ball, 4 pockets, one shot, MAES and Supervised Learning using GAN
 Scenes: BilliardSLAndMAES-OneShotSimplified-GAN
 
+This case is a demo to show what kind of data GAN can generate. See the image below.
+<p align="center">
+    <img src="Images/IntelligentPool/GAN.png" 
+        alt="GAN" 
+        width="600" border="10" />
+</p>
+<p align="center">
+  <em>Comparison of heatmap and GAN samples </em>
+</p>
+
+On the heat map at the top right corner, the green dots are samples generated by GAN. You can see that they are in mostly in the white area of the heap map, whcih makes sense.