An actor-critic algorithm for multi-agent learning in queue-based stochastic games

被引：9

作者：

Sundar, D. Krishna ^{[1
]}

Ravikumar, K. ^{[1
]}

机构：

[1] Indian Inst Management Bangalore, Bangalore 560076, Karnataka, India

来源：

NEUROCOMPUTING | 2014年 / 127卷

关键词：

Service markets; Queues; Dynamic pricing; Stochastic games; Learning in games; Reinforcement learning;

D O I：

10.1016/j.neucom.2013.07.020

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We consider state-dependent pricing in a two-player service market stochastic game where state of the game and its transition dynamics are modeled using a semi-Markovian queue. We propose a multi-time scale actor-critic based reinforcement algorithm for multi-agent learning under self-play and provide experimental results on Nash convergence. (C) 2013 Elsevier B.V. All rights reserved.

引用

页码：258 / 265

页数：8

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