Why the ‘selfish’ optimizing agents could solve the decentralized reinforcement learning problems-Reference-Cited by-同舟云学术

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Why the ‘selfish’ optimizing agents could solve the decentralized reinforcement learning problems

Published:2019-05-16 Issue:2 Volume:32 Page:143-159
ISSN:1875-8452
Container-title:AI Communications
language:
Short-container-title:AIC

Author:

Nguyen Thanh¹,Mukhopadhyay Snehasis¹,Babbar-Sebens Meghna²

Affiliation:

1. Department of Computer and Information Science, Indiana University Purdue University Indianapolis, 723 W Michigan St SL 280 Indianapolis, Indiana 46202, United States. E-mails: thamnguy@iupui.edu, smukhopa@iupui.edu

2. Department of Water Resources Engineering, Oregon State University, 1691 SW Campus Way, Owen Hall 211, Corvallis, Oregon, 97331, United States. E-mail: meghna@oregonstate.edu

Publisher

IOS Press

Subject

Artificial Intelligence

Reference43 articles.

1. Nearly optimal control laws for nonlinear systems with saturating actuators using a neural network HJB approach;Abu-Khalaf;Automatica,2005

2. N.M. Alexandrov and M.Y. Hussaini, Multidisciplinary Design Optimization: State of the Art, SIAM, 1997.

3. Analytical and computational aspects of collaborative optimization for multidisciplinary design;Alexandrov;AIAA Journal,2002

4. Decentralized Q-learning for stochastic teams and games;Arslan;IEEE Transactions on Automatic Control,2016

5. D.P. Atherton and D.P. Atherton, Nonlinear Control Engineering, Van Nostrand Reinhold, New York, 1982.

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