The analysis and performance evaluation of the pheromone-Q-learning algorithm
Abstract: The paper presents the pheromone-Q-learning (Phe-Q) algorithm, a variation of Q-learning. The technique was developed to allow agents to communicate and jointly learn to solve a problem. Phe-Q learning combines the standard Q-learning technique with a synthetic pheromone that acts as a com...
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Gespeichert in:
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Monekosso, N. [verfasserIn] Remagnino, P. [verfasserIn] |
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| Erschienen: |
Oxford, UK: Blackwell Publishing ; 2004 |
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| Umfang: |
Online-Ressource |
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| Reproduktion: |
2004 ; Blackwell Publishing Journal Backfiles 1879-2005 |
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| Übergeordnetes Werk: |
In: Expert systems - Oxford [u.a.] : Wiley-Blackwell, 1997, 21(2004), 2, Seite 0 |
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volume:21 ; year:2004 ; number:2 ; pages:0 |
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10.1111/j.1468-0394.2004.00265.x |
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| 520 | |a Abstract: The paper presents the pheromone-Q-learning (Phe-Q) algorithm, a variation of Q-learning. The technique was developed to allow agents to communicate and jointly learn to solve a problem. Phe-Q learning combines the standard Q-learning technique with a synthetic pheromone that acts as a communication medium speeding up the learning process of cooperating agents. The Phe-Q update equation includes a belief factor that reflects the confidence an agent has in the pheromone (the communication medium) deposited in the environment by other agents. With the Phe-Q update equation, the speed of convergence towards an optimal solution depends on a number of parameters including the number of agents solving a problem, the amount of pheromone deposit, the diffusion into neighbouring cells and the evaporation rate. The main objective of this paper is to describe and evaluate the performance of the Phe-Q algorithm. The paper demonstrates the improved performance of cooperating Phe-Q agents over non-cooperating agents. The paper also shows how Phe-Q learning can be improved by optimizing all the parameters that control the use of the synthetic pheromone. | ||
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10.1111/j.1468-0394.2004.00265.x doi (DE-627)NLEJ242374905 DE-627 ger DE-627 rakwb Monekosso, N. verfasserin aut The analysis and performance evaluation of the pheromone-Q-learning algorithm Oxford, UK Blackwell Publishing 2004 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Abstract: The paper presents the pheromone-Q-learning (Phe-Q) algorithm, a variation of Q-learning. The technique was developed to allow agents to communicate and jointly learn to solve a problem. Phe-Q learning combines the standard Q-learning technique with a synthetic pheromone that acts as a communication medium speeding up the learning process of cooperating agents. The Phe-Q update equation includes a belief factor that reflects the confidence an agent has in the pheromone (the communication medium) deposited in the environment by other agents. With the Phe-Q update equation, the speed of convergence towards an optimal solution depends on a number of parameters including the number of agents solving a problem, the amount of pheromone deposit, the diffusion into neighbouring cells and the evaporation rate. The main objective of this paper is to describe and evaluate the performance of the Phe-Q algorithm. The paper demonstrates the improved performance of cooperating Phe-Q agents over non-cooperating agents. The paper also shows how Phe-Q learning can be improved by optimizing all the parameters that control the use of the synthetic pheromone. 2004 Blackwell Publishing Journal Backfiles 1879-2005 |2004|||||||||| multi-agent systems Remagnino, P. verfasserin aut In Expert systems Oxford [u.a.] : Wiley-Blackwell, 1997 21(2004), 2, Seite 0 Online-Ressource (DE-627)NLEJ243925662 (DE-600)2016958-9 1468-0394 nnns volume:21 year:2004 number:2 pages:0 http://dx.doi.org/10.1111/j.1468-0394.2004.00265.x text/html Verlag Deutschlandweit zugänglich Volltext GBV_USEFLAG_U ZDB-1-DJB GBV_NL_ARTICLE AR 21 2004 2 0 |
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10.1111/j.1468-0394.2004.00265.x doi (DE-627)NLEJ242374905 DE-627 ger DE-627 rakwb Monekosso, N. verfasserin aut The analysis and performance evaluation of the pheromone-Q-learning algorithm Oxford, UK Blackwell Publishing 2004 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Abstract: The paper presents the pheromone-Q-learning (Phe-Q) algorithm, a variation of Q-learning. The technique was developed to allow agents to communicate and jointly learn to solve a problem. Phe-Q learning combines the standard Q-learning technique with a synthetic pheromone that acts as a communication medium speeding up the learning process of cooperating agents. The Phe-Q update equation includes a belief factor that reflects the confidence an agent has in the pheromone (the communication medium) deposited in the environment by other agents. With the Phe-Q update equation, the speed of convergence towards an optimal solution depends on a number of parameters including the number of agents solving a problem, the amount of pheromone deposit, the diffusion into neighbouring cells and the evaporation rate. The main objective of this paper is to describe and evaluate the performance of the Phe-Q algorithm. The paper demonstrates the improved performance of cooperating Phe-Q agents over non-cooperating agents. The paper also shows how Phe-Q learning can be improved by optimizing all the parameters that control the use of the synthetic pheromone. 2004 Blackwell Publishing Journal Backfiles 1879-2005 |2004|||||||||| multi-agent systems Remagnino, P. verfasserin aut In Expert systems Oxford [u.a.] : Wiley-Blackwell, 1997 21(2004), 2, Seite 0 Online-Ressource (DE-627)NLEJ243925662 (DE-600)2016958-9 1468-0394 nnns volume:21 year:2004 number:2 pages:0 http://dx.doi.org/10.1111/j.1468-0394.2004.00265.x text/html Verlag Deutschlandweit zugänglich Volltext GBV_USEFLAG_U ZDB-1-DJB GBV_NL_ARTICLE AR 21 2004 2 0 |
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10.1111/j.1468-0394.2004.00265.x doi (DE-627)NLEJ242374905 DE-627 ger DE-627 rakwb Monekosso, N. verfasserin aut The analysis and performance evaluation of the pheromone-Q-learning algorithm Oxford, UK Blackwell Publishing 2004 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Abstract: The paper presents the pheromone-Q-learning (Phe-Q) algorithm, a variation of Q-learning. The technique was developed to allow agents to communicate and jointly learn to solve a problem. Phe-Q learning combines the standard Q-learning technique with a synthetic pheromone that acts as a communication medium speeding up the learning process of cooperating agents. The Phe-Q update equation includes a belief factor that reflects the confidence an agent has in the pheromone (the communication medium) deposited in the environment by other agents. With the Phe-Q update equation, the speed of convergence towards an optimal solution depends on a number of parameters including the number of agents solving a problem, the amount of pheromone deposit, the diffusion into neighbouring cells and the evaporation rate. The main objective of this paper is to describe and evaluate the performance of the Phe-Q algorithm. The paper demonstrates the improved performance of cooperating Phe-Q agents over non-cooperating agents. The paper also shows how Phe-Q learning can be improved by optimizing all the parameters that control the use of the synthetic pheromone. 2004 Blackwell Publishing Journal Backfiles 1879-2005 |2004|||||||||| multi-agent systems Remagnino, P. verfasserin aut In Expert systems Oxford [u.a.] : Wiley-Blackwell, 1997 21(2004), 2, Seite 0 Online-Ressource (DE-627)NLEJ243925662 (DE-600)2016958-9 1468-0394 nnns volume:21 year:2004 number:2 pages:0 http://dx.doi.org/10.1111/j.1468-0394.2004.00265.x text/html Verlag Deutschlandweit zugänglich Volltext GBV_USEFLAG_U ZDB-1-DJB GBV_NL_ARTICLE AR 21 2004 2 0 |
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10.1111/j.1468-0394.2004.00265.x doi (DE-627)NLEJ242374905 DE-627 ger DE-627 rakwb Monekosso, N. verfasserin aut The analysis and performance evaluation of the pheromone-Q-learning algorithm Oxford, UK Blackwell Publishing 2004 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Abstract: The paper presents the pheromone-Q-learning (Phe-Q) algorithm, a variation of Q-learning. The technique was developed to allow agents to communicate and jointly learn to solve a problem. Phe-Q learning combines the standard Q-learning technique with a synthetic pheromone that acts as a communication medium speeding up the learning process of cooperating agents. The Phe-Q update equation includes a belief factor that reflects the confidence an agent has in the pheromone (the communication medium) deposited in the environment by other agents. With the Phe-Q update equation, the speed of convergence towards an optimal solution depends on a number of parameters including the number of agents solving a problem, the amount of pheromone deposit, the diffusion into neighbouring cells and the evaporation rate. The main objective of this paper is to describe and evaluate the performance of the Phe-Q algorithm. The paper demonstrates the improved performance of cooperating Phe-Q agents over non-cooperating agents. The paper also shows how Phe-Q learning can be improved by optimizing all the parameters that control the use of the synthetic pheromone. 2004 Blackwell Publishing Journal Backfiles 1879-2005 |2004|||||||||| multi-agent systems Remagnino, P. verfasserin aut In Expert systems Oxford [u.a.] : Wiley-Blackwell, 1997 21(2004), 2, Seite 0 Online-Ressource (DE-627)NLEJ243925662 (DE-600)2016958-9 1468-0394 nnns volume:21 year:2004 number:2 pages:0 http://dx.doi.org/10.1111/j.1468-0394.2004.00265.x text/html Verlag Deutschlandweit zugänglich Volltext GBV_USEFLAG_U ZDB-1-DJB GBV_NL_ARTICLE AR 21 2004 2 0 |
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Abstract: The paper presents the pheromone-Q-learning (Phe-Q) algorithm, a variation of Q-learning. The technique was developed to allow agents to communicate and jointly learn to solve a problem. Phe-Q learning combines the standard Q-learning technique with a synthetic pheromone that acts as a communication medium speeding up the learning process of cooperating agents. The Phe-Q update equation includes a belief factor that reflects the confidence an agent has in the pheromone (the communication medium) deposited in the environment by other agents. With the Phe-Q update equation, the speed of convergence towards an optimal solution depends on a number of parameters including the number of agents solving a problem, the amount of pheromone deposit, the diffusion into neighbouring cells and the evaporation rate. The main objective of this paper is to describe and evaluate the performance of the Phe-Q algorithm. The paper demonstrates the improved performance of cooperating Phe-Q agents over non-cooperating agents. The paper also shows how Phe-Q learning can be improved by optimizing all the parameters that control the use of the synthetic pheromone. |
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Abstract: The paper presents the pheromone-Q-learning (Phe-Q) algorithm, a variation of Q-learning. The technique was developed to allow agents to communicate and jointly learn to solve a problem. Phe-Q learning combines the standard Q-learning technique with a synthetic pheromone that acts as a communication medium speeding up the learning process of cooperating agents. The Phe-Q update equation includes a belief factor that reflects the confidence an agent has in the pheromone (the communication medium) deposited in the environment by other agents. With the Phe-Q update equation, the speed of convergence towards an optimal solution depends on a number of parameters including the number of agents solving a problem, the amount of pheromone deposit, the diffusion into neighbouring cells and the evaporation rate. The main objective of this paper is to describe and evaluate the performance of the Phe-Q algorithm. The paper demonstrates the improved performance of cooperating Phe-Q agents over non-cooperating agents. The paper also shows how Phe-Q learning can be improved by optimizing all the parameters that control the use of the synthetic pheromone. |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">NLEJ242374905</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20210707154130.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">120427s2004 xx |||||o 00| ||und c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1111/j.1468-0394.2004.00265.x</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)NLEJ242374905</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Monekosso, N.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">The analysis and performance evaluation of the pheromone-Q-learning algorithm</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Oxford, UK</subfield><subfield code="b">Blackwell Publishing</subfield><subfield code="c">2004</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract: The paper presents the pheromone-Q-learning (Phe-Q) algorithm, a variation of Q-learning. The technique was developed to allow agents to communicate and jointly learn to solve a problem. Phe-Q learning combines the standard Q-learning technique with a synthetic pheromone that acts as a communication medium speeding up the learning process of cooperating agents. The Phe-Q update equation includes a belief factor that reflects the confidence an agent has in the pheromone (the communication medium) deposited in the environment by other agents. With the Phe-Q update equation, the speed of convergence towards an optimal solution depends on a number of parameters including the number of agents solving a problem, the amount of pheromone deposit, the diffusion into neighbouring cells and the evaporation rate. The main objective of this paper is to describe and evaluate the performance of the Phe-Q algorithm. The paper demonstrates the improved performance of cooperating Phe-Q agents over non-cooperating agents. The paper also shows how Phe-Q learning can be improved by optimizing all the parameters that control the use of the synthetic pheromone.</subfield></datafield><datafield tag="533" ind1=" " ind2=" "><subfield code="d">2004</subfield><subfield code="f">Blackwell Publishing Journal Backfiles 1879-2005</subfield><subfield code="7">|2004||||||||||</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">multi-agent systems</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Remagnino, P.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Expert systems</subfield><subfield code="d">Oxford [u.a.] : Wiley-Blackwell, 1997</subfield><subfield code="g">21(2004), 2, Seite 0</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)NLEJ243925662</subfield><subfield code="w">(DE-600)2016958-9</subfield><subfield code="x">1468-0394</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:21</subfield><subfield code="g">year:2004</subfield><subfield code="g">number:2</subfield><subfield code="g">pages:0</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://dx.doi.org/10.1111/j.1468-0394.2004.00265.x</subfield><subfield code="q">text/html</subfield><subfield code="x">Verlag</subfield><subfield code="z">Deutschlandweit zugänglich</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-1-DJB</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_NL_ARTICLE</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">21</subfield><subfield code="j">2004</subfield><subfield code="e">2</subfield><subfield code="h">0</subfield></datafield></record></collection>
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