Random Neural Networks for the Adaptive Control of Packet Networks

  • Michael Gellman
  • Peixiang Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4131)


The Random Neural Network (RNN) has been used in a wide variety of applications, including image compression, texture generation, pattern recognition, and so on. Our work focuses on the use of the RNN as a routing decision maker which uses Reinforcement Learning (RL) techniques to explore a search space (i.e. the set of all possible routes) to find the optimal route in terms of the Quality of Service metrics that are most important to the underlying traffic. We have termed this algorithm as the Cognitive Packet Network (CPN), and have shown in previous works its application to a variety of network domains. In this paper, we present a set of experiments which demonstrate how CPN performs in a realistic environment compared to a priori-computed optimal routes. We show that RNN with RL can autonomously learn the best route in the network simply through exploration in a very short time-frame. We also demonstrate the quickness with which our algorithm is able to adapt to a disruption along its current route, switching to the new optimal route in the network. These results serve as strong evidence for the benefits of the RNN Reinforcement Learning algorithm which we employ.


Adaptive Control Reinforcement Learn Optimal Route Good Route Reinforcement Learn Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Michael Gellman
    • 1
  • Peixiang Liu
    • 1
  1. 1.Dept. of Electrical & Electronic EngImperial College London 

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