Text

Improving Data-Driven Reinforcement Learning in Wireless IoT Systems Using Domain Knowledge

Reinforcement learning (RL) algorithms are purely data-driven and do not leverage any domain knowledge about the nature of the available actions, the system's state transition dynamics, and its cost/reward function. This severely penalizes their ability to meet critical requirements of emerging wireless applications, due to the inefficiency with which these algorithms learn from their interactions with the environment. In this article, we describe how data-driven RL algorithms can be improved by systematically integrating basic system models into the learning process. Our proposed approach uses real-time data in conjunction with knowledge about the underlying communication system to achieve orders of magnitude improvement in key performance metrics, such as convergence speed and compute/memory complexity, relative to well-established RL benchmarks.

Ketersediaan

Informasi Detail

Judul Seri

IEEE COMMUNICATIONS MAGAZINE; Vol.59 No.11 November 2021

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 95-101

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

MEASUREMENT
REAL-TIME SYSTEMS
WIRELESS COMMUNICATION
REINFORCEMENT LEARNING
HEURISTIC ALGORITHMS
COMPUTATIONAL MODELING
BENCHMARK TESTING

Info Detail Spesifik

DOI: 10.1109/MCOM.111.2000949

Pernyataan Tanggungjawab

Nicholas Mastronarde, Nikhilesh Sharma, Jacob Chakareski

Versi lain/terkait

Lampiran Berkas

Komentar