سال انتشار:

2020

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عنوان انگلیسی مقاله:

Coordinated behavior of cooperative agents using deep reinforcement learning

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ترجمه فارسی عنوان مقاله:

رفتار هماهنگ عوامل تعاونی با استفاده از یادگیری تقویتی عمیق

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منبع:

Sciencedirect - Elsevier - Neurocomputing, 396 (2020) 230-240. doi:10.1016/j.neucom.2018.08.094

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نویسنده:

Elhadji Amadou Oury Diallo ∗, Ayumi Sugiyama , Toshiharu Sugawara

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چکیده انگلیسی:

In this work, we focus on an environment where multiple agents with complementary capabilities co- operate to generate non-conflicting joint actions that achieve a specific target. The central problem ad- dressed is how several agents can collectively learn to coordinate their actions such that they complete a given task together without conflicts. However, sequential decision-making under uncertainty is one of the most challenging issues for intelligent cooperative systems. To address this, we propose a multi-agent concurrent framework where agents learn coordinated behaviors in order to divide their areas of respon- sibility. The proposed framework is an extension of some recent deep reinforcement learning algorithms such as DQN, double DQN, and dueling network architectures. Then, we investigate how the learned be- haviors change according to the dynamics of the environment, reward scheme, and network structures. Next, we show how agents behave and choose their actions such that the resulting joint actions are op- timal. We finally show that our method can lead to stable solutions in our specific environment.
Keywords: Deep reinforcement learning | Multi-agent systems | Cooperation | Coordination

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قیمت: رایگان

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توضیحات اضافی:

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