Esmat Pakizeh

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One of the most influential points in cooperative learning is the type of exchanging information. If the content of exchanging information among agents is rich, cooperation gives rise to better results. To extract proper knowledge of agents during the cooperation process, some expertness measures that assign expertness levels to the other agents are used.(More)
Temporal difference and eligibility traces are of the most common approaches to solve reinforcement learning problems. However, except in the case of Q-learning, there are no studies about using these two approaches in a cooperative multi-agent learning setting. This paper addresses this shortcoming by using temporal difference and eligibility traces as the(More)
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