Results 241 to 250 of about 137,924 (252)

Evolutionary game theory and multi-agent reinforcement learning

The Knowledge Engineering Review, 2005
In this paper we survey the basics of reinforcement learning and (evolutionary) game theory, applied to the field of multi-agent systems. This paper contains three parts. We start with an overview on the fundamentals of reinforcement learning. Next we summarize the most important aspects of evolutionary game theory. Finally, we discuss the state-of-the-
Tuyls, Karl, Nowe, Ann
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Multi-agent reinforcement learning for microgrids

IEEE PES General Meeting, 2010
This paper presents a general framework for Microgrids control based on Multi Agent System Technology. The proposed architecture is capable to integrate several functionalities, adaptable to the complexity and the size of the Microgrid. To achieve this, the idea of layered learning is used, where the various controls and actions of the agents are ...
A L Dimeas, N D Hatziargyriou
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Multi-agent Reinforcement Learning: An Overview

2010
Los sistemas multiagente se pueden utilizar para abordar problemas en una variedad de dominios, incluidos la robótica, el control distribuido, las telecomunicaciones y la economía. La complejidad de muchas tareas que surgen en estos dominios hace que sean difíciles de resolver con comportamientos de agentes preprogramados.
Lucian Buşoniu, Robert Babuška, Bart De Schutter   +2 more
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S-learning: A multi-agent reinforcement learning method

2000
oz S-OGRENME: BİR ÇOKLU-ETMEN TAKVIYE-OGRENME METODU Kuter, Uğur Yüksek Lisans, Bilgisayar Mühendisliği Bölümü Tez Yöneticisi: Doç. Dr. Faruk Polat Haziran 2000, 48 sayfa Çoklu-Etmen Sistemlerinde öğrenme günümüzde önem verilen bir araştırma konusudur.
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Multi-Agent Reinforcement Learning

The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec), 2017
Yuta KAJII, Kazuaki YAMADA
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Multi-Agent Reinforcement Learning

2022
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Multi-agent Exploration with Reinforcement Learning

2022 30th Mediterranean Conference on Control and Automation (MED), 2022
Alkis Sygkounas, Dimitris Tsipianitis, George Nikolakopoulos, Charalampos P. Bechlioulis   +3 more
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Towards Robust Multi-Agent Reinforcement Learning

Proceedings of the AAAI Symposium Series
Stochastic gradient descent (SGD) is at the heart of large-scale distributed machine learning paradigms such as federated learning (FL). In these applications, the task of training high-dimensional weight vectors is distributed among several workers that exchange information over networks of limited bandwidth.
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Federated, multi-agent, deep reinforcement learning

Το τοπίο της τεχνητής νοημοσύνης (ΤΝ) αναδιαμορφώνεται από την Ομόσπονδη Μάθηση (ΟΜ), μια αποκεντρωμένη προσέγγιση στη μηχανική μάθηση (ΜΜ) που ενισχύει την ιδιωτικότητα δεδομένων και τη συνεργατική εκπαίδευση μοντέλων. Αυτή η διατριβή εξετάζει τις προκλήσεις και τις δυνατότητες της ΟΜ, επικεντρώνοντας στην βελτιστοποίηση της αποδοτικότητας ...
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Hierarchical multi agent reinforcement learning

2000
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