A multi-agent cooperative reinforcement learning model using a hierarchy of consultants, tutors and workers

Abstract The hierarchical organisation of distributed systems can provide an efficient decomposition for machine learning. This paper proposes an algorithm for cooperative policy construction for independent learners, named Q-learning with aggregation (QA-learning). The algorithm is based on a distr...
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Autor*in:	Abed-alguni, Bilal H. [verfasserIn] Chalup, Stephan K. Henskens, Frans A. Paul, David J.

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2015

Schlagwörter:	Reinforcement learning Q-Learning Multi-agent system Distributed system Markov decision process Factored Markov decision process

Anmerkung:	© The Author(s) 2015

Übergeordnetes Werk:	Enthalten in: Vietnam journal of computer science - Berlin : SpringerOpen, 2014, 2(2015), 4 vom: 05. Juli, Seite 213-226
Übergeordnetes Werk:	volume:2 ; year:2015 ; number:4 ; day:05 ; month:07 ; pages:213-226

Links:	Volltext

DOI / URN:	10.1007/s40595-015-0045-x

Katalog-ID:	SPR036702579

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spelling	10.1007/s40595-015-0045-x doi (DE-627)SPR036702579 (SPR)s40595-015-0045-x-e DE-627 ger DE-627 rakwb eng Abed-alguni, Bilal H. verfasserin aut A multi-agent cooperative reinforcement learning model using a hierarchy of consultants, tutors and workers 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2015 Abstract The hierarchical organisation of distributed systems can provide an efficient decomposition for machine learning. This paper proposes an algorithm for cooperative policy construction for independent learners, named Q-learning with aggregation (QA-learning). The algorithm is based on a distributed hierarchical learning model and utilises three specialisations of agents: workers, tutors and consultants. The consultant agent incorporates the entire system in its problem space, which it decomposes into sub-problems that are assigned to the tutor and worker agents. The QA-learning algorithm aggregates the Q-tables of worker agents into a central repository managed by their tutor agent. Each tutor’s Q-table is then incorporated into the consultant’s Q-table, resulting in a Q-table for the entire problem. The algorithm was tested using a distributed hunter prey problem, and experimental results show that QA-learning converges to a solution faster than single agent Q-learning and some famous cooperative Q-learning algorithms. Reinforcement learning (dpeaa)DE-He213 Q-Learning (dpeaa)DE-He213 Multi-agent system (dpeaa)DE-He213 Distributed system (dpeaa)DE-He213 Markov decision process (dpeaa)DE-He213 Factored Markov decision process (dpeaa)DE-He213 Chalup, Stephan K. aut Henskens, Frans A. aut Paul, David J. aut Enthalten in Vietnam journal of computer science Berlin : SpringerOpen, 2014 2(2015), 4 vom: 05. Juli, Seite 213-226 (DE-627)782645380 (DE-600)2765089-3 2196-8896 nnns volume:2 year:2015 number:4 day:05 month:07 pages:213-226 https://dx.doi.org/10.1007/s40595-015-0045-x kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2 2015 4 05 07 213-226
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allfieldsSound	10.1007/s40595-015-0045-x doi (DE-627)SPR036702579 (SPR)s40595-015-0045-x-e DE-627 ger DE-627 rakwb eng Abed-alguni, Bilal H. verfasserin aut A multi-agent cooperative reinforcement learning model using a hierarchy of consultants, tutors and workers 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2015 Abstract The hierarchical organisation of distributed systems can provide an efficient decomposition for machine learning. This paper proposes an algorithm for cooperative policy construction for independent learners, named Q-learning with aggregation (QA-learning). The algorithm is based on a distributed hierarchical learning model and utilises three specialisations of agents: workers, tutors and consultants. The consultant agent incorporates the entire system in its problem space, which it decomposes into sub-problems that are assigned to the tutor and worker agents. The QA-learning algorithm aggregates the Q-tables of worker agents into a central repository managed by their tutor agent. Each tutor’s Q-table is then incorporated into the consultant’s Q-table, resulting in a Q-table for the entire problem. The algorithm was tested using a distributed hunter prey problem, and experimental results show that QA-learning converges to a solution faster than single agent Q-learning and some famous cooperative Q-learning algorithms. Reinforcement learning (dpeaa)DE-He213 Q-Learning (dpeaa)DE-He213 Multi-agent system (dpeaa)DE-He213 Distributed system (dpeaa)DE-He213 Markov decision process (dpeaa)DE-He213 Factored Markov decision process (dpeaa)DE-He213 Chalup, Stephan K. aut Henskens, Frans A. aut Paul, David J. aut Enthalten in Vietnam journal of computer science Berlin : SpringerOpen, 2014 2(2015), 4 vom: 05. Juli, Seite 213-226 (DE-627)782645380 (DE-600)2765089-3 2196-8896 nnns volume:2 year:2015 number:4 day:05 month:07 pages:213-226 https://dx.doi.org/10.1007/s40595-015-0045-x kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2 2015 4 05 07 213-226
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source	Enthalten in Vietnam journal of computer science 2(2015), 4 vom: 05. Juli, Seite 213-226 volume:2 year:2015 number:4 day:05 month:07 pages:213-226
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topic_facet	Reinforcement learning Q-Learning Multi-agent system Distributed system Markov decision process Factored Markov decision process
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container_title	Vietnam journal of computer science
authorswithroles_txt_mv	Abed-alguni, Bilal H. @@aut@@ Chalup, Stephan K. @@aut@@ Henskens, Frans A. @@aut@@ Paul, David J. @@aut@@
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author	Abed-alguni, Bilal H.
spellingShingle	Abed-alguni, Bilal H. misc Reinforcement learning misc Q-Learning misc Multi-agent system misc Distributed system misc Markov decision process misc Factored Markov decision process A multi-agent cooperative reinforcement learning model using a hierarchy of consultants, tutors and workers
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topic_title	A multi-agent cooperative reinforcement learning model using a hierarchy of consultants, tutors and workers Reinforcement learning (dpeaa)DE-He213 Q-Learning (dpeaa)DE-He213 Multi-agent system (dpeaa)DE-He213 Distributed system (dpeaa)DE-He213 Markov decision process (dpeaa)DE-He213 Factored Markov decision process (dpeaa)DE-He213
topic	misc Reinforcement learning misc Q-Learning misc Multi-agent system misc Distributed system misc Markov decision process misc Factored Markov decision process
topic_unstemmed	misc Reinforcement learning misc Q-Learning misc Multi-agent system misc Distributed system misc Markov decision process misc Factored Markov decision process
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title	A multi-agent cooperative reinforcement learning model using a hierarchy of consultants, tutors and workers
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title_full	A multi-agent cooperative reinforcement learning model using a hierarchy of consultants, tutors and workers
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title_sort	multi-agent cooperative reinforcement learning model using a hierarchy of consultants, tutors and workers
title_auth	A multi-agent cooperative reinforcement learning model using a hierarchy of consultants, tutors and workers
abstract	Abstract The hierarchical organisation of distributed systems can provide an efficient decomposition for machine learning. This paper proposes an algorithm for cooperative policy construction for independent learners, named Q-learning with aggregation (QA-learning). The algorithm is based on a distributed hierarchical learning model and utilises three specialisations of agents: workers, tutors and consultants. The consultant agent incorporates the entire system in its problem space, which it decomposes into sub-problems that are assigned to the tutor and worker agents. The QA-learning algorithm aggregates the Q-tables of worker agents into a central repository managed by their tutor agent. Each tutor’s Q-table is then incorporated into the consultant’s Q-table, resulting in a Q-table for the entire problem. The algorithm was tested using a distributed hunter prey problem, and experimental results show that QA-learning converges to a solution faster than single agent Q-learning and some famous cooperative Q-learning algorithms. © The Author(s) 2015
abstractGer	Abstract The hierarchical organisation of distributed systems can provide an efficient decomposition for machine learning. This paper proposes an algorithm for cooperative policy construction for independent learners, named Q-learning with aggregation (QA-learning). The algorithm is based on a distributed hierarchical learning model and utilises three specialisations of agents: workers, tutors and consultants. The consultant agent incorporates the entire system in its problem space, which it decomposes into sub-problems that are assigned to the tutor and worker agents. The QA-learning algorithm aggregates the Q-tables of worker agents into a central repository managed by their tutor agent. Each tutor’s Q-table is then incorporated into the consultant’s Q-table, resulting in a Q-table for the entire problem. The algorithm was tested using a distributed hunter prey problem, and experimental results show that QA-learning converges to a solution faster than single agent Q-learning and some famous cooperative Q-learning algorithms. © The Author(s) 2015
abstract_unstemmed	Abstract The hierarchical organisation of distributed systems can provide an efficient decomposition for machine learning. This paper proposes an algorithm for cooperative policy construction for independent learners, named Q-learning with aggregation (QA-learning). The algorithm is based on a distributed hierarchical learning model and utilises three specialisations of agents: workers, tutors and consultants. The consultant agent incorporates the entire system in its problem space, which it decomposes into sub-problems that are assigned to the tutor and worker agents. The QA-learning algorithm aggregates the Q-tables of worker agents into a central repository managed by their tutor agent. Each tutor’s Q-table is then incorporated into the consultant’s Q-table, resulting in a Q-table for the entire problem. The algorithm was tested using a distributed hunter prey problem, and experimental results show that QA-learning converges to a solution faster than single agent Q-learning and some famous cooperative Q-learning algorithms. © The Author(s) 2015
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title_short	A multi-agent cooperative reinforcement learning model using a hierarchy of consultants, tutors and workers
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A multi-agent cooperative reinforcement learning model using a hierarchy of consultants, tutors and workers

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