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Register a new userNeuro-Symbolic Approaches for Text-Based Policy Learning
النهج العصبية الرمزية لتعلم السياسة القائمة على النص
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برزت الألعاب القائمة على النصوص (TBGS) كخطط مهم لتعزيز التعزيز (RL) في مجال اللغة الطبيعية.الأساليب السابقة باستخدام سياسات الإجراءات القائم على LSTM غير قابلة لإرسالها وغالبا ما تتجاوزت ألعاب التدريب التي تظهر أداء ضعيف لألعاب الاختبار غير المرئية.نقدم سياسة الإجراءات الرمزية للبيئات النصية (لائحة)، والتي تتعلم قواعد سياسة العمل القابلة للتفسير من التجريدات الرمزية للملاحظات النصية لتحسين التعميم.نحن نبحث عن طريقة لتعلم القاعدة الرمزية المتفوقة في نهاية إلى نهاية وإظهار أن هذه السياسات الرمزية تتفوق على الأساليب السابقة من أحدث الأحوال الفنية في RL القائمة على الرسائل النصية لبيئة جامع العملة من 5-10x ألعاب تدريب أقل.بالإضافة إلى ذلك، توفر طريقتنا قواعد سياسية مفهومة للإنسان والتي يمكن التحقق منها بسهولة من أجل الاتساق المنطقي ويمكن تصحيحها بسهولة.
Text-Based Games (TBGs) have emerged as important testbeds for reinforcement learning (RL) in the natural language domain. Previous methods using LSTM-based action policies are uninterpretable and often overfit the training games showing poor performance to unseen test games. We present SymboLic Action policy for Textual Environments (SLATE), that learns interpretable action policy rules from symbolic abstractions of textual observations for improved generalization. We outline a method for end-to-end differentiable symbolic rule learning and show that such symbolic policies outperform previous state-of-the-art methods in text-based RL for the coin collector environment from 5-10x fewer training games. Additionally, our method provides human-understandable policy rules that can be readily verified for their logical consistency and can be easily debugged.
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