TCP-drinc: Smart congestion control based on deep reinforcement learning

Abstract

As wired/wireless networks become more and more complex, the fundamental assumptions made by many existing TCP variants may not hold true anymore. In this paper, we develop a model-free, smart congestion control algorithm based on deep reinforcement learning, which has a high potential in dealing with the complex and dynamic network environment. We present TCP-Deep ReInforcement learNing-based Congestion control (Drinc) which learns from past experience in the form of a set of measured features to decide how to adjust the congestion window size. We present the TCP-Drinc design and validate its performance with extensive ns-3 simulations and comparison with five benchmark schemes.