Çok robotlu görev atama probleminde merkezi Q-öğrenme kullanmak için etkili bir yöntem

Abstract

Çok robotlu sistemlerde Q-öğrenme yönteminin kullanımı oldukça problemlidir. Çok robotlu sistemlerde, robotun bağımsız karar verme ve hareket etme mekanizmaları nedeniyle dinamik ve kısmen gözlemlenebilir yapıya sahiptir. Oysa, Q-öğrenme yöntemi teorik olarak Markovian olarak nitelendirilebilecek ortamlar üzerinde tanımlanmıştır. Çok robotlu sistemlerde Q-öğrenmeyi uygulamanın bir yolu, merkezi öğrenmedir. Merkezi öğrenme, tüm sistemin durum uzayı ve tüm robotların tümleşik hareket uzayları için optimal Q-değerlerini öğrenir. Bu durumda, sistem statik olarak değerlendirilmekte ve optimal çözüm yakınsama mümkün olmaktadır. Ancak, merkezi öğrenme, çevre hakkında tam bilgi edinmeyi, robotlar arası iyi bir haberleşme sağlanmasını ve iyi hesaplama gücü gerektirir. Özellikle büyük sistemler için, robot sayısındaki artışla birlikte üstel büyüyen öğrenme uzayı boyutu nedeniyle hesaplama maliyeti çok yüksek olmaktadır. Bu çalışmada önerilen yaklaşım olan subG-CQL, sistemin görev yapma yeteneklerini olumsuz yönde etkilemeden genel sistemi küçük boyutlu alt gruplara ayırır. Her bir alt grup daha az sayıda robottan oluşur, daha az görev yapar ve kendi ekibi için merkezi bir şekilde öğrenir. Böylece öğrenme alanı boyutu makul bir düzeye indirilir ve gerekli iletişim aynı alt gruptaki robotlarla sınırlı kalır. Merkezi öğrenmenin kullanılması nedeniyle başarılı sonuçlara ulaşılması beklenmektedir. Deneysel çalışmalar, önerilen algoritmanın sistemin görev atama performansında artış ve sistem kaynaklarının verimli kullanımını sağladığını göstermektedir.

Keywords

• Çok robotlu sistemler
• Görev atama
• Q-Öğrenme
• Merkezi öğrenme

An effective method to use centralized Q-learning in multi-robot task allocation

Abstract

The use of Q-learning methods in multi-robot systems is a challenging area. Multi-robot systems have dynamic and partially observable nature because of robot’s independent decision-making and acting mechanisms. Whereas, Q-learning is defined on Markovian environments theoretically. One way to apply Q-learning in multi robot systems is centralized learning. It learns optimal Q-values for state space of overall system and joint action spaces of all agents. In this case, the system can be considered as stationary and optimal solutions can be converged. But, centralized learning requires full knowledge of the environment, perfect inter-robot communication and good computational power. Especially for large systems, the computational cost becomes huge because of exponentially growing learning space size with the number of robots. The proposed approach in this study, subG-CQL, divides the overall system into small-sized sub-groups without adversely affecting the system's task performing abilities. Each sub-group consists of less number of robots performing less tasks and learns in centralized manner for its own team. So, the learning space dimension is reduced to a reasonable level and required communication remains limited to the robots in the same the sub-group. Due the centralized learning is used, it is expected that the successful results are achieved. Experimental studies show that the proposed algorithm provides increase in the task assignment performance of the system and efficient use of system resources.

Keywords

• Multi-Robot systems
• Task allocation
• Q-Learning
• Centralized learning

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