Paper Info
Title
Communication-Efficient Policy Gradient Methods for Distributed Reinforcement Learning
Abstract
This article deals with distributed policy optimization in reinforcement learning, which involves a central controller and a group of learners. In particular, two typical settings encountered in several applications are considered: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multiagent reinforcement learning</i> (RL) and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">parallel RL</i> , where frequent information exchanges between the learners and the controller are required. For many practical distributed systems, however, the overhead caused by these frequent communication exchanges is considerable, and becomes the bottleneck of the overall performance. To address this challenge, a novel policy gradient approach is developed for solving distributed RL. The novel approach adaptively skips the policy gradient communication during iterations, and can reduce the communication overhead without degrading learning performance. It is established analytically that: i) the novel algorithm has a convergence rate identical to that of the plain-vanilla policy gradient; while ii) if the distributed learners are heterogeneous in terms of their reward functions, the number of communication rounds needed to achieve a desirable learning accuracy is markedly reduced. Numerical experiments corroborate the communication reduction attained by the novel algorithm compared to alternatives.
Year
DOI
Venue
2022
10.1109/TCNS.2021.3078100
IEEE Transactions on Control of Network Systems
Keywords
DocType
Volume
Communication-efficient learning,distributed learning,multiagent,policy gradient,reinforcement learning
Journal
9
Issue
ISSN
Citations 
2
2325-5870
2
PageRank 
References 
Authors
0.38
10
4
Name
Order
Citations
PageRank
Tianyi Chen1437.52
Kaiqing Zhang24813.02
G. B. Giannakis3114641206.47
Tamer Basar43497402.11