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@@ -398,7 +398,7 @@ <h3 class="title is-4">Ant - Velocity</h3>
  <h2 class="title is-3">Conclusion</h2>
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  <p>
- In this work, we introduce a novel Quality-Diversity algorithm fully formalized as an actor-critic method. This approach leverages both a performance critic and a behavior critic to learn high-performing and diverse behaviors. Within this framework, the actor optimizes an objective that seamlessly integrates these two critics with a Lagrange multiplier, using constrained optimization to (1) maximize return and (2) execute diverse skills.
+ In this work, we introduce a novel Quality-Diversity algorithm fully formalized as an actor-critic method. This approach leverages both a <span style="color: #e1144b;">performance critic</span> and a <span style="color: #0053d6;">behavior critic</span> to learn high-performing and diverse behaviors. Within this framework, the actor optimizes an objective that seamlessly integrates these two critics with a <b>Lagrange multiplier</b>, using constrained optimization to (1) <span style="color: #e1144b;">maximize return</span> and (2) <span style="color: #0053d6;">execute diverse skills</span>.
  </p>
  <p>
  We demonstrate that our approach is competitive compared to traditional Quality-Diversity methods. Quantitative results demonstrate that QDAC is competitive in adaptation tasks, while qualitative analyses reveal a range of diverse and remarkable behaviors.