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Multi-Agent Machine Learning in Self-Organizing Systems

Hejazi Hosseini, Ehsan | 2020

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 53276 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Nobakhti, Amin; Bagheri Shouraki, Saeed
  7. Abstract:
  8. 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 approximation like artificial neural networks (ANN) and 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. Although there exist a few previous works focusing on solving similar problems, this thesis 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 also verify the stability of our approach and show the high speed of finding the optimal solution as a result of using the transfer learning method
  9. Keywords:
  10. Multiagent System ; Transfer Learning ; Game Theory ; Reinforcement Learning ; Self-Organizing Map (SOM)

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