Update README.md

README.md

@@ -10,6 +10,8 @@ Valentyn N Sichkar. Reinforcement Learning Algorithms for global path planning /
 
 * The research results for Neural Network Knowledge Based system for the tasks of collision avoidance is put in separate repository and is available here: https://github.com/sichkar-valentyn/Matlab_implementation_of_Neural_Networks
 
+* The study of Semantic Web languages OWL and RDF for Knowledge representation of Alarm-Warning System is put in separate repository and is available here: https://github.com/sichkar-valentyn/Knowledge_Base_Represented_by_Semantic_Web_Language
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 * The study of Neural Networks for Computer Vision in autonomous vehicles and robotics is put in separate repository and is available here: https://github.com/sichkar-valentyn/Neural_Networks_for_Computer_Vision
 
 ## Description
@@ -77,12 +79,15 @@ Environment-1 with mobile robot, goal and obstacles
 
 <img src="images/Environment-1.gif" alt="RL_Q-Learning_E-1" width=362 height=391> <img src="images/Environment-1.png" alt="RL_Q-Learning_E-1" width=362 height=391>
 
+<br/>
 
 ### <a name="Q-learning algorithm resulted chart for the environment-1">Q-learning algorithm resulted chart for the environment-1</a>
 Represents number of episodes via number of steps and number of episodes via cost for each episode
 
 ![RL_Q-Learning_C-1](images/Charts-1.png)
 
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 ### <a name="Final Q-table with values from the final shortest route for environment-1">Final Q-table with values from the final shortest route for environment-1</a>
 ![RL_Q-Learning_T-1](images/Q-Table-E-1.png)
 <br/>Looking at the values of the table we can see the decision for the next action made by agent (mobile robot). The sequence of final actions to reach the goal after the Q-table is filled with knowledge is the following: *down-right-down-down-down-right-down-right-down-right-down-down-right-right-up-up.*
@@ -95,11 +100,15 @@ Bigger environment-2 with more obstacles
 
 ![RL_Q-Learning_E-2](images/Environment-2.png)
 
+<br/>
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 ### <a name="Q-learning algorithm resulted chart for the environment-2">Q-learning algorithm resulted chart for the environment-2</a>
 Represents number of episodes via number of steps and number of episodes via cost for each episode
 
 ![RL_Q-Learning_C-2](images/Charts-2.png)
 
+<br/>
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 ### <a name="Final Q-table with values from the final shortest route for environment-1">Final Q-table with values from the final shortest route for environment-1</a>
 ![RL_Q-Learning_T-2](images/Q-Table-E-2.png)