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main/environments/all-environments/index.html

@@ -345,7 +345,7 @@ <h1>Available environments<a class="headerlink" href="#available-environments" t
 <td><p><code class="docutils literal notranslate"><span class="pre">[fish_amount,</span> <span class="pre">wood_amount]</span></code></p></td>
 <td><p>ESR environment, the agent must collect fish and wood to light a fire and eat. From <a class="reference external" href="https://www.researchgate.net/publication/328718263_Multi-objective_Reinforcement_Learning_for_the_Expected_Utility_of_the_Return">Roijers et al. 2018</a>.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference external" href="https://mo-gymnasium.farama.org/environments/breakable-bottles/"><code class="docutils literal notranslate"><span class="pre">breakable-bottles-v0</span></code></a> <br><img src="https://raw.githubusercontent.com/Farama-Foundation/MO-Gymnasium/main/docs/_static/screenshots/breakable-bottles.gif" width="200px"></p></td>
+<tr class="row-even"><td><p><a class="reference external" href="https://mo-gymnasium.farama.org/environments/breakable-bottles/"><code class="docutils literal notranslate"><span class="pre">breakable-bottles-v0</span></code></a> <br><img src="https://raw.githubusercontent.com/Farama-Foundation/MO-Gymnasium/main/docs/_static/videos/breakable-bottles.gif" width="200px"></p></td>
 <td><p>Discrete (Dictionary) / Discrete</p></td>
 <td><p><code class="docutils literal notranslate"><span class="pre">[time_penalty,</span> <span class="pre">bottles_delivered,</span> <span class="pre">potential]</span></code></p></td>
 <td><p>Gridworld with 5 cells. The agents must collect bottles from the source location and deliver to the destination. From <a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S0952197621000336">Vamplew et al. 2021</a>.</p></td>