Advances in Reinforcement Learning by Abdelhamid Mellouk

By Abdelhamid Mellouk

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The Computation-Intensive Task needs very long time to run, and also needs high performance computer to support, such as the finite element analysis. For the description of learning model, we introduce some definitions: Computing Node (CN) is defined as CN (id, CT, Am, AS), where id denotes the identifier of CN; CT denotes the type of computing node; Am denotes the main control agent of CN; AS is the set of agents running on CN. Computer cluster (CC) is defined as CC (Ma, CS), where Ma denotes the main computer of CC; CS= {CN1, CN2…CNp} denotes the set of all computing nodes which CC includes; Computing Agent (CA) is defined as CA (id, PRG, BDI, KS, CE), where id denotes the identifier of CA; PRG denotes the executable program set of CA; BDI is the description of its BDI; KS is its knowledge set; CE is its configuration environment.

Furthermore, nodes mobility can lead to the creation of new routes involving nodes with a high residual energy. EDAR quickly detects and selects these high quality nodes, thus allowing it to resist more efficiently to frequent route changes. 11 considers the mean delivery rate achieved when the mobility increases. Under a static scenario, all protocols achieve a very good delivery rate, EDAR being the only one higher than 90%. As expected, the delivery rate dramatically decreases with an increasing mobility, but the decrease is less significant for adaptive protocols, especially for EDAR.

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.

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