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       <svg width="15" height="15" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2.2"><path d="M14 2H6a2 2 0 0 0-2 2v16a2 2 0 0 0 2 2h12a2 2 0 0 0 2-2V8z"/><polyline points="14 2 14 8 20 8"/><line x1="16" y1="13" x2="8" y2="13"/><line x1="16" y1="17" x2="8" y2="17"/><polyline points="10 9 9 9 8 9"/></svg>
       PDF Paper
     </a>
+    <a href="https://github.com/yxc20089/OpenRA-RL" class="btn btn-secondary" target="_blank">
+      <svg width="15" height="15" viewBox="0 0 24 24" fill="currentColor"><path d="M12 2C6.477 2 2 6.477 2 12c0 4.418 2.865 8.166 6.839 9.489.5.092.682-.217.682-.482 0-.237-.009-.868-.013-1.703-2.782.604-3.369-1.342-3.369-1.342-.454-1.154-1.11-1.462-1.11-1.462-.908-.62.069-.608.069-.608 1.003.07 1.531 1.03 1.531 1.03.892 1.529 2.341 1.087 2.91.831.092-.646.35-1.086.636-1.336-2.22-.253-4.555-1.11-4.555-4.943 0-1.091.39-1.984 1.029-2.683-.103-.253-.446-1.27.098-2.647 0 0 .84-.269 2.75 1.025A9.578 9.578 0 0 1 12 6.836a9.59 9.59 0 0 1 2.504.337c1.909-1.294 2.747-1.025 2.747-1.025.546 1.377.203 2.394.1 2.647.64.699 1.028 1.592 1.028 2.683 0 3.842-2.339 4.687-4.566 4.935.359.309.678.919.678 1.852 0 1.336-.012 2.415-.012 2.743 0 .267.18.578.688.48C19.138 20.161 22 16.416 22 12c0-5.523-4.477-10-10-10z"/></svg>
+      Environment (GitHub)
+    </a>
     <a href="https://github.com/huixu11/openra-rl-challenge" class="btn btn-secondary" target="_blank">
       <svg width="15" height="15" viewBox="0 0 24 24" fill="currentColor"><path d="M12 2C6.477 2 2 6.477 2 12c0 4.418 2.865 8.166 6.839 9.489.5.092.682-.217.682-.482 0-.237-.009-.868-.013-1.703-2.782.604-3.369-1.342-3.369-1.342-.454-1.154-1.11-1.462-1.11-1.462-.908-.62.069-.608.069-.608 1.003.07 1.531 1.03 1.531 1.03.892 1.529 2.341 1.087 2.91.831.092-.646.35-1.086.636-1.336-2.22-.253-4.555-1.11-4.555-4.943 0-1.091.39-1.984 1.029-2.683-.103-.253-.446-1.27.098-2.647 0 0 .84-.269 2.75 1.025A9.578 9.578 0 0 1 12 6.836a9.59 9.59 0 0 1 2.504.337c1.909-1.294 2.747-1.025 2.747-1.025.546 1.377.203 2.394.1 2.647.64.699 1.028 1.592 1.028 2.683 0 3.842-2.339 4.687-4.566 4.935.359.309.678.919.678 1.852 0 1.336-.012 2.415-.012 2.743 0 .267.18.578.688.48C19.138 20.161 22 16.416 22 12c0-5.523-4.477-10-10-10z"/></svg>
-      Code (GitHub)
+      Training (GitHub)
     </a>
     <a href="https://youtu.be/YaO-KyHiXfo" class="btn btn-secondary" target="_blank">
       <svg width="15" height="15" viewBox="0 0 24 24" fill="currentColor"><path d="M23.498 6.186a3.016 3.016 0 0 0-2.122-2.136C19.505 3.545 12 3.545 12 3.545s-7.505 0-9.377.505A3.017 3.017 0 0 0 .502 6.186C0 8.07 0 12 0 12s0 3.93.502 5.814a3.016 3.016 0 0 0 2.122 2.136c1.871.505 9.376.505 9.376.505s7.505 0 9.377-.505a3.015 3.015 0 0 0 2.122-2.136C24 15.93 24 12 24 12s0-3.93-.502-5.814zM9.545 15.568V8.432L15.818 12l-6.273 3.568z"/></svg>
       Demo Video
     </a>
-    <a href="https://huggingface.co/openenv" class="btn btn-secondary" target="_blank">🤗 OpenEnv Hub</a>
+    <a href="https://huggingface.co/spaces/openra-rl/openra-rl-challenge" class="btn btn-secondary" target="_blank">🤗 OpenEnv Hub</a>
   </div>
 </header>
 
@@ -499,7 +503,7 @@
     <h2><span class="sec-num">03</span>Demonstration</h2>
     <div class="section-divider"></div>
 
-    <p>We have developed a Python normal AI bot that can beat OpenRA easy AI bot. The <a href="https://github.com/huixu11/openra-rl-challenge" target="_blank">code is on GitHub</a> and the <a href="https://youtu.be/YaO-KyHiXfo" target="_blank">replay can be watched on YouTube</a>.</p>
+    <p>We have developed a Python normal AI bot that can beat OpenRA easy AI bot. The environment code is on <a href="https://github.com/yxc20089/OpenRA-RL" target="_blank">GitHub (OpenRA-RL)</a> and also deployed as a <a href="https://huggingface.co/spaces/openra-rl/openra-rl-challenge" target="_blank">live HuggingFace Space</a>; the training/challenge code is at <a href="https://github.com/huixu11/openra-rl-challenge" target="_blank">GitHub (openra-rl-challenge)</a>. The replay can be watched <a href="https://youtu.be/YaO-KyHiXfo" target="_blank">on YouTube</a>.</p>
 
     <p>To exercise the platform end-to-end through the OpenEnv interface, we deploy Qwen3 32B served locally via Ollama as an LLM agent playing the Allied faction against the built-in Beginner AI on a 128×128 map. The agent receives structured game-state observations as tool responses and issues actions through the MCP tools. We run five episodes under two timing regimes: <strong>Games 1–2 use a 30-minute time limit, while Games 3–5 use a 5-minute limit.</strong></p>
 
@@ -651,7 +655,7 @@ env.close()</code></pre>
 </main>
 
 <footer>
-  <p>OpenRA-RL · April 2026 · <a href="https://huggingface.co/openenv" target="_blank">OpenEnv Hub</a> · <a href="https://github.com/huixu11/openra-rl-challenge" target="_blank">GitHub</a> · <a href="_OpenENV_Blog__OpenRA_RL.pdf" target="_blank">PDF</a></p>
+  <p>OpenRA-RL · April 2026 · <a href="https://huggingface.co/openenv" target="_blank">OpenEnv Hub</a> · <a href="https://huggingface.co/spaces/openra-rl/openra-rl-challenge" target="_blank">HF Space</a> · <a href="https://github.com/yxc20089/OpenRA-RL" target="_blank">Environment (GitHub)</a> · <a href="https://github.com/huixu11/openra-rl-challenge" target="_blank">Training (GitHub)</a> · <a href="_OpenENV_Blog__OpenRA_RL.pdf" target="_blank">PDF</a></p>
   <p style="margin-top:8px;opacity:0.5;">Xiaochuang Yuan, Hui Xu, Yiyu Tian, Siqi Zhang, Ruiyue Wang, Kaiser Sun</p>
 </footer>