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@@ -56,6 +56,9 @@ <h3 id="related-works">Related works:</h3>
   <li>
     <p>The research results for Neural Network Knowledge Based system for the tasks of collision avoidance is put in separate repository and is available here: <a href="https://github.com/sichkar-valentyn/Matlab_implementation_of_Neural_Networks">Matlab implementation of Neural Networks</a></p>
   </li>
+  <li>
+    <p>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: <a href="https://github.com/sichkar-valentyn/Knowledge_Base_Represented_by_Semantic_Web_Language">Knowledge Base Represented by Semantic Web Language</a></p>
+  </li>
   <li>
     <p>The study of Neural Networks for Computer Vision in autonomous vehicles and robotics is put in separate repository and is available here: <a href="https://github.com/sichkar-valentyn/Neural_Networks_for_Computer_Vision">Neural Networks for Computer Vision</a></p>
   </li>
@@ -136,11 +139,15 @@ <h3 id="rl-q-learning-environment-1-experimental-results"><a name="RL Q-Learning
 
 <p><img src="/Reinforcement_Learning_in_Python/images/Environment-1.gif" alt="Environment-1" width="312" height="341" />  <img src="/Reinforcement_Learning_in_Python/images/Environment-1.png" alt="Environment-1" width="312" height="341" /></p>
 
+<p><br /></p>
+
 <h3 id="q-learning-algorithm-resulted-chart-for-the-environment-1"><a name="Q-learning algorithm resulted chart for the environment-1">Q-learning algorithm resulted chart for the environment-1</a></h3>
 <p>Represents number of episodes via number of steps and number of episodes via cost for each episode</p>
 
 <p><img src="/Reinforcement_Learning_in_Python/images/Charts-1.png" alt="RL_Q-Learning_C-1" /></p>
 
+<p><br /></p>
+
 <h3 id="final-q-table-with-values-from-the-final-shortest-route-for-environment-1"><a name="Final Q-table with values from the final shortest route for environment-1">Q-table with values from the final shortest route for environment-1</a></h3>
 <p><img src="/Reinforcement_Learning_in_Python/images/Q-Table-E-1.png" alt="RL_Q-Learning_T-1" />
 <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: <em>down-right-down-down-down-right-down-right-down-right-down-down-right-right-up-up.</em>
@@ -153,11 +160,15 @@ <h3 id="rl-q-learning-environment-2-experimental-results"><a name="RL Q-Learning
 
 <p><img src="/Reinforcement_Learning_in_Python/images/Environment-2.png" alt="RL_Q-Learning_E-2" /></p>
 
+<p><br /></p>
+
 <h3 id="q-learning-algorithm-resulted-chart-for-the-environment-2"><a name="Q-learning algorithm resulted chart for the environment-2">Q-learning algorithm resulted chart for the environment-2</a></h3>
 <p>Represents number of episodes via number of steps and number of episodes via cost for each episode</p>
 
 <p><img src="/Reinforcement_Learning_in_Python/images/Charts-2.png" alt="RL_Q-Learning_C-2" /></p>
 
+<p><br /></p>
+
 <h3 id="final-q-table-with-values-from-the-final-shortest-route-for-environment-1-1"><a name="Final Q-table with values from the final shortest route for environment-1">Q-table with values from the final shortest route for environment-1</a></h3>
 <p><img src="/Reinforcement_Learning_in_Python/images/Q-Table-E-2.png" alt="RL_Q-Learning_T-2" /></p>