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Register a new userEfficient Dialogue Complementary Policy Learning via Deep Q-network Policy and Episodic Memory Policy
التعلم الفعال للحوار السياسي التكميلي عبر سياسة شبكة Q-Network العميقة وسياسة الذاكرة العرضية
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أظهر تعلم التعزيز العميق إمكانات كبيرة في سياسات الحوار التدريبية. ومع ذلك، فإن أدائها المواتي يأتي بتكلفة العديد من جولات التفاعل. تعتمد معظم أساليب سياسة الحوار الحالية على نظام تعليمي واحد، في حين أن الدماغ البشري يحتوي على نظامين لتعلم وذاكرة متخصصين، يدعمان لإيجاد حلول جيدة دون الحاجة إلى أمثلة غزيرة. مستوحاة من الدماغ البشري، تقترح هذه الورقة إطار عمل لتعلم السياسات التكميلي الرواية (CPL)، والتي تستغل المزايا التكميلية لسياسة الذاكرة العرضية (EM) وسياسة شبكة Q-Network (DQN) العميقة لتحقيق تعلم سياسة حوار سريعة وفعالة وبعد من أجل التنسيق بين السياسة، اقترحنا وحدة تحكم الثقة للسيطرة على الوقت التكميلي وفقا لفعولتها النسبية في مراحل مختلفة. علاوة على ذلك، يتم اقتراح اتصال الذاكرة وتقليم الوقت لضمان التعميم المرن والتكيف للسياسة EM في مهام الحوار. تظهر النتائج التجريبية على ثلاث مجموعات بيانات الحوار أن طريقتنا تتفوق بشكل كبير على الطرق الحالية التي تعتمد على نظام تعليمي واحد.
Deep reinforcement learning has shown great potential in training dialogue policies. However, its favorable performance comes at the cost of many rounds of interaction. Most of the existing dialogue policy methods rely on a single learning system, while the human brain has two specialized learning and memory systems, supporting to find good solutions without requiring copious examples. Inspired by the human brain, this paper proposes a novel complementary policy learning (CPL) framework, which exploits the complementary advantages of the episodic memory (EM) policy and the deep Q-network (DQN) policy to achieve fast and effective dialogue policy learning. In order to coordinate between the two policies, we proposed a confidence controller to control the complementary time according to their relative efficacy at different stages. Furthermore, memory connectivity and time pruning are proposed to guarantee the flexible and adaptive generalization of the EM policy in dialog tasks. Experimental results on three dialogue datasets show that our method significantly outperforms existing methods relying on a single learning system.
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