Reinforcement Learning for Adaptive Routing
<|reference_start|>Reinforcement Learning for Adaptive Routing: Reinforcement learning means learning a policy--a mapping of observations into actions--based on feedback from the environment. The learning can be viewed as browsing a set of policies while evaluating them by trial through interaction with the environment. We present an application of gradient ascent algorithm for reinforcement learning to a complex domain of packet routing in network communication and compare the performance of this algorithm to other routing methods on a benchmark problem.<|reference_end|>
@article{peshkin2007reinforcement,
    title={Reinforcement Learning for Adaptive Routing},
    author={Leonid Peshkin and Virginia Savova},
    journal={In Proceedings of the Intnl Joint Conf on Neural Networks (IJCNN),
  2002},
    year={2007},
    archivePrefix={arXiv},
    eprint={cs/0703138},
    primaryClass={cs.LG cs.AI cs.NI}
}