Network Congestion Control with Deep Reinforcement Learning

PhD Thesis Proposal Defence


Title: "Network Congestion Control with Deep Reinforcement Learning"

by

Mr. Han TIAN


Abstract:

Recent years have witnessed a plethora of learning-based solutions, especially 
ones adopting deep reinforcement learning (DRL), for congestion control, which 
show outstanding performance improvement compared to traditional TCP schemes. 
However, several challenges still remain when incorporating deep reinforcement 
learning into the classic control task in networking. Some of them are 
intrinsic and have not been solved by the current DRL-based CC schemes, e.g., 
the fairness issue; Some are introduced by learning-based algorithms adopting 
deep neural networks, e.g., the overhead issue; Furthermore, new demands arise 
to extend capability and flexibility of CC schemes, e.g., multiple objectives. 
These problems hinder network transport designers and operators from putting 
DRL-based solutions into practice in the real world.

This thesis presents our effort in solving the above problems. For the fairness 
issue, we propose Astraea, a novel learning-based solution based on multi-agent 
reinforcement learning that ensures fast convergence to fairness with 
stability; For the overhead issue, we propose Spine, a hierarchical congestion 
control algorithm that fully utilizes the performance gain from DRL but with 
ultra-low overhead; For the multi-objective requirement from applications, we 
propose MOCC, a congestion control scheme based on multi-objective 
reinforcement learning that fits to various performance objectives in one 
single model. In the future, we will continue to work towards providing a 
practical, efficient and flexible DRL-based congestion control scheme with 
consistently high performance across various network conditions.


Date:  			Friday, 26 August 2022

Time:                  	2:00pm - 4:00pm

Zoom Meeting:		https://hkust.zoom.us/j/7939516131

Committee Members:	Prof. Qiang Yang (Supervisor)
 			Prof. Kai Chen (Supervisor)
 			Prof. Gary Chan (Chairperson)
 			Prof. Ke Yi


**** ALL are Welcome ****