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.
Carlstr "om @cite_5 introduces another rl strategy based on decomposition called predictive gain scheduling. The control problem of admission control is decomposed into a time-series prediction of near-future call arrival rates and precomputation of control policies for Poisson call arrival processes. This approach results in faster learning without performance loss. Online convergence rate increases 50 times on a simulated link with capacity @math units sec.
@cite_5
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When a user requests. a connection to another user or a computer in a communications network, a routing algorithm selects a path for transferring the resulting data stream. If all suitable paths ar ...