Advances in Reinforcement Learning by Abdelhamid Mellouk

By Abdelhamid Mellouk

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Algorithm3 (Adjusting weight and life for rules) Suppose that Y1 is the castoff threshold, and Y2 is the mature threshold; Q(urt) is the reinforcement function and Q(urt)>0, and urt is the resources utilization rate; MaxWeight is the maximum of the rule weight, and MinWeight is the minimum of the rule weight, and let MinWeight

We expect that the different protocols will react and behave differently to protect the network’s lifetime, while guaranteeing a good QoS. To this end, we are interested in three metrics to evaluate EDAR and compare it to the other protocols. These metrics were carefully chosen in order to give a broad idea of the improvements achieved by our protocol: • The Energy consumption represents the energy (in mJ) spent by the nodes to route the packets to the sink. It includes both data packets and signalling packet.

S. 9-44, ISSN: 0885-6125 (Print) 1573-0565 (Online). H. 279-292, ISSN: 08856125 (Print) 1573-0565 (Online). An Introduction to MultiAgent Systems, John Wiley & Sons (Chichester, England). ISBN: 0 47149691X. A. The concept of a linguistic variable and its application to approximate reason-ing, Information Sciences, Vol. 8, No. 3. (1975), pp. 199-249. R. &Wooldridge, M. 127-141, ISBN: 3-540-415947, Limerick, Ireland, 2000, Springer-Verlag New York, Inc. Secaucus, NJ, USA. Organizational rules as an abstraction for the analysis and design of multi-agent systems.

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