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Multi-agent machine learning in self-organizing systems

Hejazi, E ; Sharif University of Technology | 2021

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  1. Type of Document: Article
  2. DOI: 10.1016/j.ins.2021.09.013
  3. Publisher: Elsevier Inc , 2021
  4. Abstract:
  5. This paper develops a novel insight and procedure that includes a variety of algorithms for finding the best solution in a structured multi-agent system with internal communications and a global purpose. In other words, it finds the optimal communication structure among agents and the optimal policy in this structure. First, a unique reinforcement learning algorithm is proposed to find the optimal policy of each agent in a fixed structure with non-linear function approximators like artificial neural networks (ANN) and with eligibility traces. Secondly, a mechanism is presented to perform self-organization based on the information of the learned policy. Finally, an algorithm that can discover an appropriate inter-structure mapping and then can transfer the previous knowledge to the new structure is developed, which increases the speed of the learning in this new environment after self-organization. This paper is one of the first works that analyzes the problem fully theoretically and devises some algorithms to find the best solution. We use a simplified version of the distributed task allocation problem (DTAP) as our case study. The experimental results verify the stability of our approach and show the high speed of finding the optimal solution as a result of using transfer learning. © 2021 Elsevier Inc
  6. Keywords:
  7. Functions ; Game theory ; Learning algorithms ; Multi agent systems ; Neural networks ; Software agents ; Structural optimization ; Communication structures ; Fixed structure ; Internal communications ; Multi agent ; Optimal communication ; Optimal policies ; Reinforcement learning algorithms ; Self organizations ; Self-organizing systems ; Transfer learning ; Reinforcement learning
  8. Source: Information Sciences ; Volume 581 , 2021 , Pages 194-214 ; 00200255 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/abs/pii/S0020025521009348