Off-Policy Learning in Contextual Bandits for Remote Electrical Tilt Optimization-Reference-Cited by-同舟云学术

Off-Policy Learning in Contextual Bandits for Remote Electrical Tilt Optimization

Published:2023-01 Issue:1 Volume:72 Page:546-556
ISSN:0018-9545
Container-title:IEEE Transactions on Vehicular Technology
language:
Short-container-title:IEEE Trans. Veh. Technol.

Author:

Vannella Filippo¹^ORCID,Jeong Jaeseong²^ORCID,Proutiere Alexandre¹^ORCID

Affiliation:

1. Division of Decision and Control Systems at the School of EECS, KTH Royal Institute of Technology, Stockholm, Sweden

2. Ericsson Research, Stockholm, Sweden

Funder

Wallenberg AI

Knut och Alice Wallenbergs Stiftelse

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Aerospace Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/25/10017140/09868108.pdf?arnumber=9868108

Reference35 articles.

1. Deep learning with logged bandit feedback;joachims;Proc Int Conf Learn Representations,0

2. The offset tree for learning with partial labels

3. A Safe Reinforcement Learning Architecture for Antenna Tilt Optimisation

4. Self-optimization of coverage and capacity based on a fuzzy neural network with cooperative reinforcement learning;shaoshuai;EURASIP J Wireless Commun Netw,2014

5. Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms

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1. Evaluation of Intrinsic Explainable Reinforcement Learning in Remote Electrical Tilt Optimization;Proceedings of Eighth International Congress on Information and Communication Technology;2023-09-15