  Multipath TCP (MPTCP) has emerged as a facilitator for harnessing and pooling
available bandwidth in wireless/wireline communication networks and in data
centers. Existing implementations of MPTCP such as, Linked Increase Algorithm
(LIA), Opportunistic LIA (OLIA) and BAlanced LInked Adaptation (BALIA) include
separate algorithms for congestion control and packet scheduling, with
pre-selected control parameters. We propose a Deep Q-Learning (DQL) based
framework for joint congestion control and packet scheduling for MPTCP. At the
heart of the solution is an intelligent agent for interface, learning and
actuation, which learns from experience optimal congestion control and
scheduling mechanism using DQL techniques with policy gradients. We provide a
rigorous stability analysis of system dynamics which provides important
practical design insights. In addition, the proposed DQL-MPTCP algorithm
utilizes the `recurrent neural network' and integrates it with `long short-term
memory' for continuously i) learning dynamic behavior of subflows (paths) and
ii) responding promptly to their behavior using prioritized experience replay.
With extensive emulations, we show that the proposed DQL-based MPTCP algorithm
outperforms MPTCP LIA, OLIA and BALIA algorithms. Moreover, the DQL-MPTCP
algorithm is robust to time-varying network characteristics, and provides
dynamic exploration and exploitation of paths.
