Campus Units

Electrical and Computer Engineering

Document Type

Conference Proceeding

Conference

11th International Conference on Wireless Communications and Signal Processing (WCSP)

Publication Version

Accepted Manuscript

Link to Published Version

https://doi.org/10.1109/WCSP.2019.8928124

Publication Date

2019

Journal or Book Title

2019 11th International Conference on Wireless Communications and Signal Processing (WCSP)

DOI

10.1109/WCSP.2019.8928124

Conference Title

11th International Conference on Wireless Communications and Signal Processing (WCSP)

Conference Date

October 23-25, 2019

City

Xi'an, China

Abstract

This work presents two reinforcement learning (RL) architectures, which mimic rational humans in the way of analyzing the available information and making decisions. The proposed algorithms are called selector-actor-critic (SAC) and tuner-actor-critic (TAC). They are obtained by modifying the well known actor-critic (AC) algorithm. SAC is equipped with an actor, a critic, and a selector. The role of the selector is to determine the most promising action at the current state based on the last estimate from the critic. TAC is model based, and consists of a tuner, a model-learner, an actor, and a critic. After receiving the approximated value of the current state-action pair from the critic and the learned model from the model-learner, the tuner uses the Bellman equation to tune the value of the current state-action pair. Then, this tuned value is used by the actor to optimize the policy. We investigate the performance of the proposed algorithms, and compare with AC algorithm to show the advantages of the proposed algorithms using numerical simulations.

Comments

This is a manuscript of a proceeding published as Masadeh, Ala'eddin, Zhengdao Wang, and Ahmed E. Kamal. "Selector-Actor-Critic and Tuner-Actor-Critic Algorithms for Reinforcement Learning." In 2019 11th International Conference on Wireless Communications and Signal Processing (WCSP). DOI: 10.1109/WCSP.2019.8928124. Posted with permission.

Rights

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Copyright Owner

IEEE

Language

en

File Format

application/pdf

Published Version

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