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design/reward_function/index.html

@@ -1180,7 +1180,7 @@ inputs must be distributed fairly to guarantee an objective comparison between d
 <li>The distance from the robot to the target and/or the light intensity are treated as global inputs.</li>
 <li>Positions and joints, which are normalized to floating-point values between 0 and 1, are considered local inputs.</li>
 <li>The reward function is centered around minimizing the distance to the goal or maximizing the movement towards the goal
-  within a finite number of timesteps $T$.</li>
+  within a finite number of timesteps <span class="arithmatex">\(T\)</span>.</li>
 <li>To motivate efficient movement, the amount of timesteps taken to reach the goal will be used as penalty.</li>
 <li>An extra penalty based on movement relative to the current step and the previous is used to penalize a movement away from the target.</li>
 </ul>
@@ -1252,6 +1252,10 @@ this down as potential future research.</p>
     
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