  Recently, much effort has been devoted by researchers from both academia and
industry to develop novel congestion control methods. LearningCC is presented
in this letter, in which the congestion control problem is solved by reinforce
learning approach. Instead of adjusting the congestion window with fixed
policy, there are serval options for an endpoint to choose. To predict the best
option is a hard task. Each option is mapped as an arm of a bandit machine. The
endpoint can learn to determine the optimal choice through trial and error
method. Experiments are performed on ns3 platform to verify the effectiveness
of LearningCC by comparing with other benchmark algorithms. Results indicate it
can achieve lower transmission delay than loss based algorithms. Especially, we
found LearningCC makes significant improvement in link suffering from random
loss.
