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Register a new userMultitasking Inhibits Semantic Drift
تعدد المهام يمنع الانجراف الدلالي
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عندما يتواصل الوكلاء الذكيون بإنجاز الأهداف المشتركة، كيف تشكل هذه الأهداف لغة الوكلاء؟ندرس ديناميات التعلم في سياسات اللغة الكامنة (LTPs)، حيث يولد وكلاء المعلمون الأوصاف الفرعية باللغة الطبيعية ووكلاء المنفذين تعيين هذه الأوصاف إلى إجراءات منخفضة المستوى.يمكن LLP حل مشاكل تعليم التعزيز الطويلة في الأفق وتقديم نموذج غني لدراسة استخدام اللغة الموجهة نحو المهام.لكن العمل السابق قد وجد أن التدريب LLP عرضة للانجراف الدلالي (استخدام الرسائل بطرق غير متناسقة مع معاني اللغة الطبيعية الأصلية).هنا، نوضح نظري وتجريبيا أن التدريب المتعدد هو مضاد فعال لهذه المشكلة: نثبت أن التدريب المتعدد يتزيل الانجراف الدلالي في عائلة مدرسية جيدا من ألعاب الإشارات، وإظهار أن التدريب المتعدد في LT LT LTWural في لعبة استراتيجية معقدة تقللالانجراف وبين تحسين كفاءة عينة.
When intelligent agents communicate to accomplish shared goals, how do these goals shape the agents' language? We study the dynamics of learning in latent language policies (LLPs), in which instructor agents generate natural-language subgoal descriptions and executor agents map these descriptions to low-level actions. LLPs can solve challenging long-horizon reinforcement learning problems and provide a rich model for studying task-oriented language use. But previous work has found that LLP training is prone to semantic drift (use of messages in ways inconsistent with their original natural language meanings). Here, we demonstrate theoretically and empirically that multitask training is an effective counter to this problem: we prove that multitask training eliminates semantic drift in a well-studied family of signaling games, and show that multitask training of neural LLPs in a complex strategy game reduces drift and while improving sample efficiency.
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