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By Abdelhamid Mellouk

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System-Level Approach to Energy Conservation”, Power Management in Mobile Devices, pp. 217-259. G. (1997). Reinforcement Learning. Ed. MIT Press. , (2005). "BeeAdHoc: an energy efficient routing algorithm for mobile ad hoc networks inspired by bee behavior", In Genetic And Evolutionary Computation Conference Proceedings, pp 153-160. 2 Cooperative Agent Learning Model in Multi-cluster Grid Qingkui Chen, Songlin Zhuang and He Jia, XiaoDong Ding School of Computer Engineering, University of Shanghai for Science and Technology China 1.

Mean delay for various mobility patterns Fig. 9 plots the mean end-to-end delay achieved by all four protocols under various speeds of the nodes. In the case of a static topology, the results for all protocols are quite similar. However, increasing the mobility reveals a clear difference in the protocols’ efficiency. The plot indicates that, for speeds above 5 m/s, the performances of Qos_AODV and SAR degrade quickly (the delay increases from 2000 to 5000 ms), while EAR and EDAR keep delivering packets on a reasonable delay, for a speed up to 10 ms/s.

However, for a network node to be able to make an optimal routing decision, according to relevant performance criteria, it requires not only up-to-date and complete knowledge of the state of the entire network but also an accurate prediction of the network dynamics during propagation of the message through the network. This problem is naturally formulated as a dynamic programming problem, which, however, is too complex to be solved exactly. Reinforcement learning (RL) is used to Wireless Networks Inductive Routing Based on Reinforcement Learning Paradigms 21 approximate the value function of dynamic programming.

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