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“School of Cognitive Sciences”

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Paper   IPM / Cognitive Sciences / 7573
School of Cognitive Sciences
  Title:   Certainty and expertness based credit assignment for cooperative Q-learning agents with an and-type task
1 . A. Harati
2 . M. Nili Ahmadabadi
  Status:   In Proceedings
  Proceeding: ICONIP'02seal'02-FSKD'02
  Year:  2002
  Supported by:  IPM
In multi agent reinforcement learning, inter agent credit assignment is a fundamental problem, since a single scalar reinforcement signal it is only reliable feedback that teams of learning agents receive. This problem is more critical in groups of independent learners with a joint task. In this research, it is assumed that a critic agent receives the environment feedback and assigns a proper credit to each agent using some measures. Three of such measures for a team of cooperative agents with a parallel and AND-type task are introduced. These measures somehow compare the agents' knowledge. One of these criteria, called Normal Expertness, is a non-relative measure while two other ones (Certainly and Relative Normal Expertness) are relative measures work better as they contain more information for the critic agent.

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