YouTube links added

gsoc/2022/posts/daniel_peix/4-2d_DQN.md

@@ -27,6 +27,7 @@ The reward function is one of the most important aspects of a Reinforcement Lear
 In order to create a decent reward function, the 2d distance is used. With two thresholds, the environment can check is the gripper is above te cube, far away from it or any other possible situation. Depending on which situation is taking place, the reward wil be different.
 
 ## Results
-TODO: YouTube links
+Start of the training: https://youtu.be/fx7trTHZsjk
+End of the training: https://youtu.be/bgIHhoa8vrQ
 
 __Daniel Peix del Río__

gsoc/2022/posts/daniel_peix/5-3d_DQN.md

@@ -22,6 +22,7 @@ In this case, the size of the observation needs to change. As we are using 3 dim
 This reward function is not as trivial as the one done for two dimensions. In this case, there is more information available from the environment, so the reward function might be a little more complicated. The data which is used in this new reward function is: 2D distance, 3D distance and the gripper's 'fingers' data. Using two values for the distance (2D and 3D) allows us to give more importance to the 2D distance over the 3D one, because is crucial to be first above the cube.
 
 ## Results
-TODO: YouTube links
+Start of the training: https://youtu.be/zBbi9Xjelkg
+End of the training: https://youtu.be/T5mk46UGFe8
 
 __Daniel Peix del Río__

gsoc/2022/posts/daniel_peix/6-4d_DQN.md

@@ -22,6 +22,8 @@ In this case, the size of the observation needs to change. As we are now using t
 This reward function is quite similar to the 3-dimensional one, just adding the griper's 'hand' info. The data which is used in this new reward function is: 2D distance, 3D distance, the gripper's 'fingers' data and the gripper's 'hand' data.
 
 ## Results
-TODO: YouTube links
+Start of the training: https://youtu.be/TjRCTKmOpRg
+Middle of the training: https://youtu.be/VOYvWodl6Ik
+End of the training: The computer clogged because CoppeliaSim used all the RAM
 
 __Daniel Peix del Río__