Abstract
A robust routing algorithm was developed based on reinforcement learning that uses (1) reward-weighted principal component analysis, which compresses the state space of a network with a large number of nodes and eliminates the adverse effects of various types of attacks or disturbance noises, (2) activity-oriented index allocation, which adaptively constructs a basis that is used for approximating routing probabilities, and (3) newly developed control space compression based on a potential model that reduces the control space for routing probabilities. This algorithm takes all the network states into account and reduces the adverse effects of disturbance noises. The algorithm thus works well, and the frequencies of causing routing loops and falling to a local optimum are reduced even if the routing information is disturbed.
