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Register a new userReading and Acting while Blindfolded: The Need for Semantics in Text Game Agents
القراءة والتصرف أثناء معصوب العينين: الحاجة إلى دلالات في وكلاء ألعاب النص
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الألعاب القائمة على النص محاكاة العالمين والتفاعل مع اللاعبين باستخدام اللغة الطبيعية. لقد استخدمها العمل الحديث كخطأ في وكلاء فهم اللغة المستقلة، مع وجود الدافع هو فهم معاني الكلمات أو الدلالات هو مكون رئيسي في كيفية فهم البشر والسبب والتصرف في هذه العوالم. ومع ذلك، لا يزال غير واضح إلى أي مدى يستخدم الوكلاء الاصطناعي الفهم الدلالي للنص. تحقيقا لهذه الغاية، نقوم بإجراء تجارب لتقليل كمية المعلومات الدلالية المتاحة لعامل التعلم. من المستغرب أن نجد أن الوكيل قادر على تحقيق درجات عالية حتى في الغياب التام للدليل اللغوي، مما يشير إلى أن الإعداد التجريبي الشهير حاليا قد تكون مصممة بشكل سيء لفهم واستفادة من نصوص اللعبة. لعلاج هذا النقص، نقترح فك تشفير ديناميات عكسية لتنظيم مساحة التمثيل وتشجيع الاستكشاف، مما يدل على تحسين الأداء في العديد من الألعاب بما في ذلك زورق الأول - نناقش الآثار المترتبة على نتائجنا لتصميم وكلاء في المستقبل مع فهم دلالي أقوى.
Text-based games simulate worlds and interact with players using natural language. Recent work has used them as a testbed for autonomous language-understanding agents, with the motivation being that understanding the meanings of words or semantics is a key component of how humans understand, reason, and act in these worlds. However, it remains unclear to what extent artificial agents utilize semantic understanding of the text. To this end, we perform experiments to systematically reduce the amount of semantic information available to a learning agent. Surprisingly, we find that an agent is capable of achieving high scores even in the complete absence of language semantics, indicating that the currently popular experimental setup and models may be poorly designed to understand and leverage game texts. To remedy this deficiency, we propose an inverse dynamics decoder to regularize the representation space and encourage exploration, which shows improved performance on several games including Zork I. We discuss the implications of our findings for designing future agents with stronger semantic understanding.
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