Holdings: Multi-agent coordination

Multi-agent coordination: a reinforcement learning approach

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Bibliographic Details
Main Authors:	Sadhu, Arup Kumar ca. 20./21. Jh (Author), Konar, Amit 1963- (Author)
Format:	Electronic eBook
Language:	English
Published:	Piscataway, NJ IEEE Press 2021 Hoboken, NJ Wiley
Subjects:	Mehragentensystem Bestärkendes Lernen > Künstliche Intelligenz
Online Access:	FCO01 FHI01 TUM01
Item Description:	Description based on publisher supplied metadata and other sources
Physical Description:	1 Online-Ressource (xxii, 296 Seiten) Illustrationen, Diagramme
ISBN:	9781119698999 9781119699026 9781119699057

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505	8		\|a Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgments -- Chapter 1 Introduction: Multi-agent Coordination by Reinforcement Learning and Evolutionary Algorithms -- 1.1 Introduction -- 1.2 Single Agent Planning -- 1.2.1 Terminologies Used in Single Agent Planning -- 1.2.2 Single Agent Search-Based Planning Algorithms -- 1.2.2.1 Dijkstra's Algorithm -- 1.2.2.2 A* (A-star) Algorithm -- 1.2.2.3 D* (D-star) Algorithm -- 1.2.2.4 Planning by STRIPS-Like Language -- 1.2.3 Single Agent RL -- 1.2.3.1 Multiarmed Bandit Problem -- 1.2.3.2 DP and Bellman Equation -- 1.2.3.3 Correlation Between RL and DP -- 1.2.3.4 Single Agent Q-Learning -- 1.2.3.5 Single Agent Planning Using Q-Learning -- 1.3 Multi-agent Planning and Coordination -- 1.3.1 Terminologies Related to Multi-agent Coordination -- 1.3.2 Classification of MAS -- 1.3.3 Game Theory for Multi-agent Coordination -- 1.3.3.1 Nash Equilibrium -- 1.3.3.2 Correlated Equilibrium -- 1.3.3.3 Static Game Examples -- 1.3.4 Correlation Among RL, DP, and GT -- 1.3.5 Classification of MARL -- 1.3.5.1 Cooperative MARL -- 1.3.5.2 Competitive MARL -- 1.3.5.3 Mixed MARL -- 1.3.6 Coordination and Planning by MAQL -- 1.3.7 Performance Analysis of MAQL and MAQL-Based Coordination -- 1.4 Coordination by Optimization Algorithm -- 1.4.1 PSO Algorithm -- 1.4.2 Firefly Algorithm -- 1.4.2.1 Initialization -- 1.4.2.2 Attraction to Brighter Fireflies -- 1.4.2.3 Movement of Fireflies -- 1.4.3 Imperialist Competitive Algorithm -- 1.4.3.1 Initialization -- 1.4.3.2 Selection of Imperialists and Colonies -- 1.4.3.3 Formation of Empires -- 1.4.3.4 Assimilation of Colonies -- 1.4.3.5 Revolution -- 1.4.3.6 Imperialistic Competition -- 1.4.4 Differential Evolution Algorithm -- 1.4.4.1 Initialization -- 1.4.4.2 Mutation -- 1.4.4.3 Recombination -- 1.4.4.4 Selection -- 1.4.5 Off-line Optimization
505	8		\|a 1.4.6 Performance Analysis of Optimization Algorithms -- 1.4.6.1 Friedman Test -- 1.4.6.2 Iman-Davenport Test -- 1.5 Summary -- References -- Chapter 2 Improve Convergence Speed of Multi-Agent Q-Learning for Cooperative Task Planning -- 2.1 Introduction -- 2.2 Literature Review -- 2.3 Preliminaries -- 2.3.1 Single Agent Q-learning -- 2.3.2 Multi-agent Q-learning -- 2.4 Proposed MAQL -- 2.4.1 Two Useful Properties -- 2.5 Proposed FCMQL Algorithms and Their Convergence Analysis -- 2.5.1 Proposed FCMQL Algorithms -- 2.5.2 Convergence Analysis of the Proposed FCMQL Algorithms -- 2.6 FCMQL-Based Cooperative Multi-agent Planning -- 2.7 Experiments and Results -- 2.8 Conclusions -- 2.9 Summary -- 2.A More Details on Experimental Results -- 2.A.1 Additional Details of Experiment 2.1 -- 2.A.2 Additional Details of Experiment 2.2 -- 2.A.3 Additional Details of Experiment 2.4 -- References -- Chapter 3 Consensus Q-Learning for Multi-agent Cooperative Planning -- 3.1 Introduction -- 3.2 Preliminaries -- 3.2.1 Single Agent Q-Learning -- 3.2.2 Equilibrium-Based Multi-agent Q-Learning -- 3.3 Consensus -- 3.4 Proposed CoQL and Planning -- 3.4.1 Consensus Q-Learning -- 3.4.2 Consensus-Based Multi-robot Planning -- 3.5 Experiments and Results -- 3.5.1 Experimental Setup -- 3.5.2 Experiments for CoQL -- 3.5.3 Experiments for Consensus-Based Planning -- 3.6 Conclusions -- 3.7 Summary -- References -- Chapter 4 An Efficient Computing of Correlated Equilibrium for Cooperative Q-Learning-Based Multi-Robot Planning -- 4.1 Introduction -- 4.2 Single-Agent Q-Learning and Equilibrium-Based MAQL -- 4.2.1 Single Agent Q-Learning -- 4.2.2 Equilibrium-Based MAQL -- 4.3 Proposed Cooperative MAQL and Planning -- 4.3.1 Proposed Schemes with Their Applicability -- 4.3.2 Immediate Rewards in Scheme-I and -II -- 4.3.3 Scheme-I-Induced MAQL -- 4.3.4 Scheme-II-Induced MAQL.
505	8		\|a 4.3.5 Algorithms for Scheme-I and II -- 4.3.6 Constraint OmegaQL-I/OmegaQL-II(COmegaQL-I/COmegaQL-II) -- 4.3.7 Convergence -- 4.3.8 Multi-agent Planning -- 4.4 Complexity Analysis -- 4.4.1 Complexity of CQL -- 4.4.1.1 Space Complexity -- 4.4.1.2 Time Complexity -- 4.4.2 Complexity of the Proposed Algorithms -- 4.4.2.1 Space Complexity -- 4.4.2.2 Time Complexity -- 4.4.3 Complexity Comparison -- 4.4.3.1 Space Complexity -- 4.4.3.2 Time Complexity -- 4.5 Simulation and Experimental Results -- 4.5.1 Experimental Platform -- 4.5.1.1 Simulation -- 4.5.1.2 Hardware -- 4.5.2 Experimental Approach -- 4.5.2.1 Learning Phase -- 4.5.2.2 Planning Phase -- 4.5.3 Experimental Results -- 4.6 Conclusion -- 4.7 Summary -- References -- Chapter 5 A Modified Imperialist Competitive Algorithm for Multi-Robot Stick-Carrying Application -- 5.1 Introduction -- 5.2 Problem Formulation for Multi-Robot Stick-Carrying -- 5.3 Proposed Hybrid Algorithm -- 5.3.1 An Overview of ICA -- 5.3.1.1 Initialization -- 5.3.1.2 Selection of Imperialists and Colonies -- 5.3.1.3 Formation of Empires -- 5.3.1.4 Assimilation of Colonies -- 5.3.1.5 Revolution -- 5.3.1.6 Imperialistic Competition -- 5.3.1.6.1 Total Empire Power Evaluation -- 5.3.1.6.2 Reassignment of Colonies and Removal of Empire -- 5.3.1.6.3 Union of Empires -- 5.4 An Overview of FA -- 5.4.1 Initialization -- 5.4.2 Attraction to Brighter Fireflies -- 5.4.3 Movement of Fireflies -- 5.5 Proposed ICFA -- 5.5.1 Assimilation of Colonies -- 5.5.1.1 Attraction to Powerful Colonies -- 5.5.1.2 Modification of Empire Behavior -- 5.5.1.3 Union of Empires -- 5.6 Simulation Results -- 5.6.1 Comparative Framework -- 5.6.2 Parameter Settings -- 5.6.3 Analysis on Explorative Power of ICFA -- 5.6.4 Comparison of Quality of the Final Solution -- 5.6.5 Performance Analysis -- 5.7 Computer Simulation and Experiment
505	8		\|a 5.7.1 Average Total Path Deviation (ATPD) -- 5.7.2 Average Uncovered Target Distance (AUTD) -- 5.7.3 Experimental Setup in Simulation Environment -- 5.7.4 Experimental Results in Simulation Environment -- 5.7.5 Experimental Setup with Khepera Robots -- 5.7.6 Experimental Results with Khepera Robots -- 5.8 Conclusion -- 5.9 Summary -- References -- Chapter 6 Conclusions and Future Directions -- 6.1 Conclusions -- 6.2 Future Directions -- Index -- EULA.
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author	Sadhu, Arup Kumar ca. 20./21. Jh Konar, Amit 1963-
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author_facet	Sadhu, Arup Kumar ca. 20./21. Jh Konar, Amit 1963-
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contents	Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgments -- Chapter 1 Introduction: Multi-agent Coordination by Reinforcement Learning and Evolutionary Algorithms -- 1.1 Introduction -- 1.2 Single Agent Planning -- 1.2.1 Terminologies Used in Single Agent Planning -- 1.2.2 Single Agent Search-Based Planning Algorithms -- 1.2.2.1 Dijkstra's Algorithm -- 1.2.2.2 A* (A-star) Algorithm -- 1.2.2.3 D* (D-star) Algorithm -- 1.2.2.4 Planning by STRIPS-Like Language -- 1.2.3 Single Agent RL -- 1.2.3.1 Multiarmed Bandit Problem -- 1.2.3.2 DP and Bellman Equation -- 1.2.3.3 Correlation Between RL and DP -- 1.2.3.4 Single Agent Q-Learning -- 1.2.3.5 Single Agent Planning Using Q-Learning -- 1.3 Multi-agent Planning and Coordination -- 1.3.1 Terminologies Related to Multi-agent Coordination -- 1.3.2 Classification of MAS -- 1.3.3 Game Theory for Multi-agent Coordination -- 1.3.3.1 Nash Equilibrium -- 1.3.3.2 Correlated Equilibrium -- 1.3.3.3 Static Game Examples -- 1.3.4 Correlation Among RL, DP, and GT -- 1.3.5 Classification of MARL -- 1.3.5.1 Cooperative MARL -- 1.3.5.2 Competitive MARL -- 1.3.5.3 Mixed MARL -- 1.3.6 Coordination and Planning by MAQL -- 1.3.7 Performance Analysis of MAQL and MAQL-Based Coordination -- 1.4 Coordination by Optimization Algorithm -- 1.4.1 PSO Algorithm -- 1.4.2 Firefly Algorithm -- 1.4.2.1 Initialization -- 1.4.2.2 Attraction to Brighter Fireflies -- 1.4.2.3 Movement of Fireflies -- 1.4.3 Imperialist Competitive Algorithm -- 1.4.3.1 Initialization -- 1.4.3.2 Selection of Imperialists and Colonies -- 1.4.3.3 Formation of Empires -- 1.4.3.4 Assimilation of Colonies -- 1.4.3.5 Revolution -- 1.4.3.6 Imperialistic Competition -- 1.4.4 Differential Evolution Algorithm -- 1.4.4.1 Initialization -- 1.4.4.2 Mutation -- 1.4.4.3 Recombination -- 1.4.4.4 Selection -- 1.4.5 Off-line Optimization 1.4.6 Performance Analysis of Optimization Algorithms -- 1.4.6.1 Friedman Test -- 1.4.6.2 Iman-Davenport Test -- 1.5 Summary -- References -- Chapter 2 Improve Convergence Speed of Multi-Agent Q-Learning for Cooperative Task Planning -- 2.1 Introduction -- 2.2 Literature Review -- 2.3 Preliminaries -- 2.3.1 Single Agent Q-learning -- 2.3.2 Multi-agent Q-learning -- 2.4 Proposed MAQL -- 2.4.1 Two Useful Properties -- 2.5 Proposed FCMQL Algorithms and Their Convergence Analysis -- 2.5.1 Proposed FCMQL Algorithms -- 2.5.2 Convergence Analysis of the Proposed FCMQL Algorithms -- 2.6 FCMQL-Based Cooperative Multi-agent Planning -- 2.7 Experiments and Results -- 2.8 Conclusions -- 2.9 Summary -- 2.A More Details on Experimental Results -- 2.A.1 Additional Details of Experiment 2.1 -- 2.A.2 Additional Details of Experiment 2.2 -- 2.A.3 Additional Details of Experiment 2.4 -- References -- Chapter 3 Consensus Q-Learning for Multi-agent Cooperative Planning -- 3.1 Introduction -- 3.2 Preliminaries -- 3.2.1 Single Agent Q-Learning -- 3.2.2 Equilibrium-Based Multi-agent Q-Learning -- 3.3 Consensus -- 3.4 Proposed CoQL and Planning -- 3.4.1 Consensus Q-Learning -- 3.4.2 Consensus-Based Multi-robot Planning -- 3.5 Experiments and Results -- 3.5.1 Experimental Setup -- 3.5.2 Experiments for CoQL -- 3.5.3 Experiments for Consensus-Based Planning -- 3.6 Conclusions -- 3.7 Summary -- References -- Chapter 4 An Efficient Computing of Correlated Equilibrium for Cooperative Q-Learning-Based Multi-Robot Planning -- 4.1 Introduction -- 4.2 Single-Agent Q-Learning and Equilibrium-Based MAQL -- 4.2.1 Single Agent Q-Learning -- 4.2.2 Equilibrium-Based MAQL -- 4.3 Proposed Cooperative MAQL and Planning -- 4.3.1 Proposed Schemes with Their Applicability -- 4.3.2 Immediate Rewards in Scheme-I and -II -- 4.3.3 Scheme-I-Induced MAQL -- 4.3.4 Scheme-II-Induced MAQL. 4.3.5 Algorithms for Scheme-I and II -- 4.3.6 Constraint OmegaQL-I/OmegaQL-II(COmegaQL-I/COmegaQL-II) -- 4.3.7 Convergence -- 4.3.8 Multi-agent Planning -- 4.4 Complexity Analysis -- 4.4.1 Complexity of CQL -- 4.4.1.1 Space Complexity -- 4.4.1.2 Time Complexity -- 4.4.2 Complexity of the Proposed Algorithms -- 4.4.2.1 Space Complexity -- 4.4.2.2 Time Complexity -- 4.4.3 Complexity Comparison -- 4.4.3.1 Space Complexity -- 4.4.3.2 Time Complexity -- 4.5 Simulation and Experimental Results -- 4.5.1 Experimental Platform -- 4.5.1.1 Simulation -- 4.5.1.2 Hardware -- 4.5.2 Experimental Approach -- 4.5.2.1 Learning Phase -- 4.5.2.2 Planning Phase -- 4.5.3 Experimental Results -- 4.6 Conclusion -- 4.7 Summary -- References -- Chapter 5 A Modified Imperialist Competitive Algorithm for Multi-Robot Stick-Carrying Application -- 5.1 Introduction -- 5.2 Problem Formulation for Multi-Robot Stick-Carrying -- 5.3 Proposed Hybrid Algorithm -- 5.3.1 An Overview of ICA -- 5.3.1.1 Initialization -- 5.3.1.2 Selection of Imperialists and Colonies -- 5.3.1.3 Formation of Empires -- 5.3.1.4 Assimilation of Colonies -- 5.3.1.5 Revolution -- 5.3.1.6 Imperialistic Competition -- 5.3.1.6.1 Total Empire Power Evaluation -- 5.3.1.6.2 Reassignment of Colonies and Removal of Empire -- 5.3.1.6.3 Union of Empires -- 5.4 An Overview of FA -- 5.4.1 Initialization -- 5.4.2 Attraction to Brighter Fireflies -- 5.4.3 Movement of Fireflies -- 5.5 Proposed ICFA -- 5.5.1 Assimilation of Colonies -- 5.5.1.1 Attraction to Powerful Colonies -- 5.5.1.2 Modification of Empire Behavior -- 5.5.1.3 Union of Empires -- 5.6 Simulation Results -- 5.6.1 Comparative Framework -- 5.6.2 Parameter Settings -- 5.6.3 Analysis on Explorative Power of ICFA -- 5.6.4 Comparison of Quality of the Final Solution -- 5.6.5 Performance Analysis -- 5.7 Computer Simulation and Experiment 5.7.1 Average Total Path Deviation (ATPD) -- 5.7.2 Average Uncovered Target Distance (AUTD) -- 5.7.3 Experimental Setup in Simulation Environment -- 5.7.4 Experimental Results in Simulation Environment -- 5.7.5 Experimental Setup with Khepera Robots -- 5.7.6 Experimental Results with Khepera Robots -- 5.8 Conclusion -- 5.9 Summary -- References -- Chapter 6 Conclusions and Future Directions -- 6.1 Conclusions -- 6.2 Future Directions -- Index -- EULA.
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Jh.</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)1249719453</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Multi-agent coordination</subfield><subfield code="b">a reinforcement learning approach</subfield><subfield code="c">Arup Kumar Sadhu, Amit Konar</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Piscataway, NJ</subfield><subfield code="b">IEEE Press</subfield><subfield code="c">2021</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Hoboken, NJ</subfield><subfield code="b">Wiley</subfield></datafield><datafield tag="264" ind1=" " ind2="4"><subfield code="c">© 2021</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (xxii, 296 Seiten)</subfield><subfield code="b">Illustrationen, Diagramme</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Description based on publisher supplied metadata and other sources</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgments -- Chapter 1 Introduction: Multi-agent Coordination by Reinforcement Learning and Evolutionary Algorithms -- 1.1 Introduction -- 1.2 Single Agent Planning -- 1.2.1 Terminologies Used in Single Agent Planning -- 1.2.2 Single Agent Search-Based Planning Algorithms -- 1.2.2.1 Dijkstra's Algorithm -- 1.2.2.2 A* (A-star) Algorithm -- 1.2.2.3 D* (D-star) Algorithm -- 1.2.2.4 Planning by STRIPS-Like Language -- 1.2.3 Single Agent RL -- 1.2.3.1 Multiarmed Bandit Problem -- 1.2.3.2 DP and Bellman Equation -- 1.2.3.3 Correlation Between RL and DP -- 1.2.3.4 Single Agent Q-Learning -- 1.2.3.5 Single Agent Planning Using Q-Learning -- 1.3 Multi-agent Planning and Coordination -- 1.3.1 Terminologies Related to Multi-agent Coordination -- 1.3.2 Classification of MAS -- 1.3.3 Game Theory for Multi-agent Coordination -- 1.3.3.1 Nash Equilibrium -- 1.3.3.2 Correlated Equilibrium -- 1.3.3.3 Static Game Examples -- 1.3.4 Correlation Among RL, DP, and GT -- 1.3.5 Classification of MARL -- 1.3.5.1 Cooperative MARL -- 1.3.5.2 Competitive MARL -- 1.3.5.3 Mixed MARL -- 1.3.6 Coordination and Planning by MAQL -- 1.3.7 Performance Analysis of MAQL and MAQL-Based Coordination -- 1.4 Coordination by Optimization Algorithm -- 1.4.1 PSO Algorithm -- 1.4.2 Firefly Algorithm -- 1.4.2.1 Initialization -- 1.4.2.2 Attraction to Brighter Fireflies -- 1.4.2.3 Movement of Fireflies -- 1.4.3 Imperialist Competitive Algorithm -- 1.4.3.1 Initialization -- 1.4.3.2 Selection of Imperialists and Colonies -- 1.4.3.3 Formation of Empires -- 1.4.3.4 Assimilation of Colonies -- 1.4.3.5 Revolution -- 1.4.3.6 Imperialistic Competition -- 1.4.4 Differential Evolution Algorithm -- 1.4.4.1 Initialization -- 1.4.4.2 Mutation -- 1.4.4.3 Recombination -- 1.4.4.4 Selection -- 1.4.5 Off-line Optimization</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">1.4.6 Performance Analysis of Optimization Algorithms -- 1.4.6.1 Friedman Test -- 1.4.6.2 Iman-Davenport Test -- 1.5 Summary -- References -- Chapter 2 Improve Convergence Speed of Multi-Agent Q-Learning for Cooperative Task Planning -- 2.1 Introduction -- 2.2 Literature Review -- 2.3 Preliminaries -- 2.3.1 Single Agent Q-learning -- 2.3.2 Multi-agent Q-learning -- 2.4 Proposed MAQL -- 2.4.1 Two Useful Properties -- 2.5 Proposed FCMQL Algorithms and Their Convergence Analysis -- 2.5.1 Proposed FCMQL Algorithms -- 2.5.2 Convergence Analysis of the Proposed FCMQL Algorithms -- 2.6 FCMQL-Based Cooperative Multi-agent Planning -- 2.7 Experiments and Results -- 2.8 Conclusions -- 2.9 Summary -- 2.A More Details on Experimental Results -- 2.A.1 Additional Details of Experiment 2.1 -- 2.A.2 Additional Details of Experiment 2.2 -- 2.A.3 Additional Details of Experiment 2.4 -- References -- Chapter 3 Consensus Q-Learning for Multi-agent Cooperative Planning -- 3.1 Introduction -- 3.2 Preliminaries -- 3.2.1 Single Agent Q-Learning -- 3.2.2 Equilibrium-Based Multi-agent Q-Learning -- 3.3 Consensus -- 3.4 Proposed CoQL and Planning -- 3.4.1 Consensus Q-Learning -- 3.4.2 Consensus-Based Multi-robot Planning -- 3.5 Experiments and Results -- 3.5.1 Experimental Setup -- 3.5.2 Experiments for CoQL -- 3.5.3 Experiments for Consensus-Based Planning -- 3.6 Conclusions -- 3.7 Summary -- References -- Chapter 4 An Efficient Computing of Correlated Equilibrium for Cooperative Q-Learning-Based Multi-Robot Planning -- 4.1 Introduction -- 4.2 Single-Agent Q-Learning and Equilibrium-Based MAQL -- 4.2.1 Single Agent Q-Learning -- 4.2.2 Equilibrium-Based MAQL -- 4.3 Proposed Cooperative MAQL and Planning -- 4.3.1 Proposed Schemes with Their Applicability -- 4.3.2 Immediate Rewards in Scheme-I and -II -- 4.3.3 Scheme-I-Induced MAQL -- 4.3.4 Scheme-II-Induced MAQL.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">4.3.5 Algorithms for Scheme-I and II -- 4.3.6 Constraint OmegaQL-I/OmegaQL-II(COmegaQL-I/COmegaQL-II) -- 4.3.7 Convergence -- 4.3.8 Multi-agent Planning -- 4.4 Complexity Analysis -- 4.4.1 Complexity of CQL -- 4.4.1.1 Space Complexity -- 4.4.1.2 Time Complexity -- 4.4.2 Complexity of the Proposed Algorithms -- 4.4.2.1 Space Complexity -- 4.4.2.2 Time Complexity -- 4.4.3 Complexity Comparison -- 4.4.3.1 Space Complexity -- 4.4.3.2 Time Complexity -- 4.5 Simulation and Experimental Results -- 4.5.1 Experimental Platform -- 4.5.1.1 Simulation -- 4.5.1.2 Hardware -- 4.5.2 Experimental Approach -- 4.5.2.1 Learning Phase -- 4.5.2.2 Planning Phase -- 4.5.3 Experimental Results -- 4.6 Conclusion -- 4.7 Summary -- References -- Chapter 5 A Modified Imperialist Competitive Algorithm for Multi-Robot Stick-Carrying Application -- 5.1 Introduction -- 5.2 Problem Formulation for Multi-Robot Stick-Carrying -- 5.3 Proposed Hybrid Algorithm -- 5.3.1 An Overview of ICA -- 5.3.1.1 Initialization -- 5.3.1.2 Selection of Imperialists and Colonies -- 5.3.1.3 Formation of Empires -- 5.3.1.4 Assimilation of Colonies -- 5.3.1.5 Revolution -- 5.3.1.6 Imperialistic Competition -- 5.3.1.6.1 Total Empire Power Evaluation -- 5.3.1.6.2 Reassignment of Colonies and Removal of Empire -- 5.3.1.6.3 Union of Empires -- 5.4 An Overview of FA -- 5.4.1 Initialization -- 5.4.2 Attraction to Brighter Fireflies -- 5.4.3 Movement of Fireflies -- 5.5 Proposed ICFA -- 5.5.1 Assimilation of Colonies -- 5.5.1.1 Attraction to Powerful Colonies -- 5.5.1.2 Modification of Empire Behavior -- 5.5.1.3 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id	DE-604.BV047442412
illustrated	Not Illustrated
index_date	2024-07-03T18:01:24Z
indexdate	2024-07-10T09:12:16Z
institution	BVB
isbn	9781119698999 9781119699026 9781119699057
language	English
oai_aleph_id	oai:aleph.bib-bvb.de:BVB01-032844564
oclc_num	1225551708
open_access_boolean
owner	DE-91 DE-BY-TUM DE-858 DE-573
owner_facet	DE-91 DE-BY-TUM DE-858 DE-573
physical	1 Online-Ressource (xxii, 296 Seiten) Illustrationen, Diagramme
psigel	ZDB-30-PQE ZDB-35-WIC ZDB-35-WEL ZDB-35-WIC FCO_PDA_WIC_Kauf ZDB-30-PQE TUM_PDA_PQE_Kauf
publishDate	2021
publishDateSearch	2021
publishDateSort	2021
publisher	IEEE Press Wiley
record_format	marc
spelling	Sadhu, Arup Kumar ca. 20./21. Jh. Verfasser (DE-588)1249719453 aut Multi-agent coordination a reinforcement learning approach Arup Kumar Sadhu, Amit Konar Piscataway, NJ IEEE Press 2021 Hoboken, NJ Wiley © 2021 1 Online-Ressource (xxii, 296 Seiten) Illustrationen, Diagramme txt rdacontent c rdamedia cr rdacarrier Description based on publisher supplied metadata and other sources Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgments -- Chapter 1 Introduction: Multi-agent Coordination by Reinforcement Learning and Evolutionary Algorithms -- 1.1 Introduction -- 1.2 Single Agent Planning -- 1.2.1 Terminologies Used in Single Agent Planning -- 1.2.2 Single Agent Search-Based Planning Algorithms -- 1.2.2.1 Dijkstra's Algorithm -- 1.2.2.2 A* (A-star) Algorithm -- 1.2.2.3 D* (D-star) Algorithm -- 1.2.2.4 Planning by STRIPS-Like Language -- 1.2.3 Single Agent RL -- 1.2.3.1 Multiarmed Bandit Problem -- 1.2.3.2 DP and Bellman Equation -- 1.2.3.3 Correlation Between RL and DP -- 1.2.3.4 Single Agent Q-Learning -- 1.2.3.5 Single Agent Planning Using Q-Learning -- 1.3 Multi-agent Planning and Coordination -- 1.3.1 Terminologies Related to Multi-agent Coordination -- 1.3.2 Classification of MAS -- 1.3.3 Game Theory for Multi-agent Coordination -- 1.3.3.1 Nash Equilibrium -- 1.3.3.2 Correlated Equilibrium -- 1.3.3.3 Static Game Examples -- 1.3.4 Correlation Among RL, DP, and GT -- 1.3.5 Classification of MARL -- 1.3.5.1 Cooperative MARL -- 1.3.5.2 Competitive MARL -- 1.3.5.3 Mixed MARL -- 1.3.6 Coordination and Planning by MAQL -- 1.3.7 Performance Analysis of MAQL and MAQL-Based Coordination -- 1.4 Coordination by Optimization Algorithm -- 1.4.1 PSO Algorithm -- 1.4.2 Firefly Algorithm -- 1.4.2.1 Initialization -- 1.4.2.2 Attraction to Brighter Fireflies -- 1.4.2.3 Movement of Fireflies -- 1.4.3 Imperialist Competitive Algorithm -- 1.4.3.1 Initialization -- 1.4.3.2 Selection of Imperialists and Colonies -- 1.4.3.3 Formation of Empires -- 1.4.3.4 Assimilation of Colonies -- 1.4.3.5 Revolution -- 1.4.3.6 Imperialistic Competition -- 1.4.4 Differential Evolution Algorithm -- 1.4.4.1 Initialization -- 1.4.4.2 Mutation -- 1.4.4.3 Recombination -- 1.4.4.4 Selection -- 1.4.5 Off-line Optimization 1.4.6 Performance Analysis of Optimization Algorithms -- 1.4.6.1 Friedman Test -- 1.4.6.2 Iman-Davenport Test -- 1.5 Summary -- References -- Chapter 2 Improve Convergence Speed of Multi-Agent Q-Learning for Cooperative Task Planning -- 2.1 Introduction -- 2.2 Literature Review -- 2.3 Preliminaries -- 2.3.1 Single Agent Q-learning -- 2.3.2 Multi-agent Q-learning -- 2.4 Proposed MAQL -- 2.4.1 Two Useful Properties -- 2.5 Proposed FCMQL Algorithms and Their Convergence Analysis -- 2.5.1 Proposed FCMQL Algorithms -- 2.5.2 Convergence Analysis of the Proposed FCMQL Algorithms -- 2.6 FCMQL-Based Cooperative Multi-agent Planning -- 2.7 Experiments and Results -- 2.8 Conclusions -- 2.9 Summary -- 2.A More Details on Experimental Results -- 2.A.1 Additional Details of Experiment 2.1 -- 2.A.2 Additional Details of Experiment 2.2 -- 2.A.3 Additional Details of Experiment 2.4 -- References -- Chapter 3 Consensus Q-Learning for Multi-agent Cooperative Planning -- 3.1 Introduction -- 3.2 Preliminaries -- 3.2.1 Single Agent Q-Learning -- 3.2.2 Equilibrium-Based Multi-agent Q-Learning -- 3.3 Consensus -- 3.4 Proposed CoQL and Planning -- 3.4.1 Consensus Q-Learning -- 3.4.2 Consensus-Based Multi-robot Planning -- 3.5 Experiments and Results -- 3.5.1 Experimental Setup -- 3.5.2 Experiments for CoQL -- 3.5.3 Experiments for Consensus-Based Planning -- 3.6 Conclusions -- 3.7 Summary -- References -- Chapter 4 An Efficient Computing of Correlated Equilibrium for Cooperative Q-Learning-Based Multi-Robot Planning -- 4.1 Introduction -- 4.2 Single-Agent Q-Learning and Equilibrium-Based MAQL -- 4.2.1 Single Agent Q-Learning -- 4.2.2 Equilibrium-Based MAQL -- 4.3 Proposed Cooperative MAQL and Planning -- 4.3.1 Proposed Schemes with Their Applicability -- 4.3.2 Immediate Rewards in Scheme-I and -II -- 4.3.3 Scheme-I-Induced MAQL -- 4.3.4 Scheme-II-Induced MAQL. 4.3.5 Algorithms for Scheme-I and II -- 4.3.6 Constraint OmegaQL-I/OmegaQL-II(COmegaQL-I/COmegaQL-II) -- 4.3.7 Convergence -- 4.3.8 Multi-agent Planning -- 4.4 Complexity Analysis -- 4.4.1 Complexity of CQL -- 4.4.1.1 Space Complexity -- 4.4.1.2 Time Complexity -- 4.4.2 Complexity of the Proposed Algorithms -- 4.4.2.1 Space Complexity -- 4.4.2.2 Time Complexity -- 4.4.3 Complexity Comparison -- 4.4.3.1 Space Complexity -- 4.4.3.2 Time Complexity -- 4.5 Simulation and Experimental Results -- 4.5.1 Experimental Platform -- 4.5.1.1 Simulation -- 4.5.1.2 Hardware -- 4.5.2 Experimental Approach -- 4.5.2.1 Learning Phase -- 4.5.2.2 Planning Phase -- 4.5.3 Experimental Results -- 4.6 Conclusion -- 4.7 Summary -- References -- Chapter 5 A Modified Imperialist Competitive Algorithm for Multi-Robot Stick-Carrying Application -- 5.1 Introduction -- 5.2 Problem Formulation for Multi-Robot Stick-Carrying -- 5.3 Proposed Hybrid Algorithm -- 5.3.1 An Overview of ICA -- 5.3.1.1 Initialization -- 5.3.1.2 Selection of Imperialists and Colonies -- 5.3.1.3 Formation of Empires -- 5.3.1.4 Assimilation of Colonies -- 5.3.1.5 Revolution -- 5.3.1.6 Imperialistic Competition -- 5.3.1.6.1 Total Empire Power Evaluation -- 5.3.1.6.2 Reassignment of Colonies and Removal of Empire -- 5.3.1.6.3 Union of Empires -- 5.4 An Overview of FA -- 5.4.1 Initialization -- 5.4.2 Attraction to Brighter Fireflies -- 5.4.3 Movement of Fireflies -- 5.5 Proposed ICFA -- 5.5.1 Assimilation of Colonies -- 5.5.1.1 Attraction to Powerful Colonies -- 5.5.1.2 Modification of Empire Behavior -- 5.5.1.3 Union of Empires -- 5.6 Simulation Results -- 5.6.1 Comparative Framework -- 5.6.2 Parameter Settings -- 5.6.3 Analysis on Explorative Power of ICFA -- 5.6.4 Comparison of Quality of the Final Solution -- 5.6.5 Performance Analysis -- 5.7 Computer Simulation and Experiment 5.7.1 Average Total Path Deviation (ATPD) -- 5.7.2 Average Uncovered Target Distance (AUTD) -- 5.7.3 Experimental Setup in Simulation Environment -- 5.7.4 Experimental Results in Simulation Environment -- 5.7.5 Experimental Setup with Khepera Robots -- 5.7.6 Experimental Results with Khepera Robots -- 5.8 Conclusion -- 5.9 Summary -- References -- Chapter 6 Conclusions and Future Directions -- 6.1 Conclusions -- 6.2 Future Directions -- Index -- EULA. Mehragentensystem (DE-588)4389058-1 gnd rswk-swf Bestärkendes Lernen Künstliche Intelligenz (DE-588)4825546-4 gnd rswk-swf Mehragentensystem (DE-588)4389058-1 s Bestärkendes Lernen Künstliche Intelligenz (DE-588)4825546-4 s DE-604 Konar, Amit 1963- Verfasser (DE-588)1063311365 aut Erscheint auch als Sadhu, Arup Kumar Multi-Agent Coordination Newark : John Wiley & Sons, Incorporated,c2020 Druck-Ausgabe 978-1-119-69903-3
spellingShingle	Sadhu, Arup Kumar ca. 20./21. Jh Konar, Amit 1963- Multi-agent coordination a reinforcement learning approach Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgments -- Chapter 1 Introduction: Multi-agent Coordination by Reinforcement Learning and Evolutionary Algorithms -- 1.1 Introduction -- 1.2 Single Agent Planning -- 1.2.1 Terminologies Used in Single Agent Planning -- 1.2.2 Single Agent Search-Based Planning Algorithms -- 1.2.2.1 Dijkstra's Algorithm -- 1.2.2.2 A* (A-star) Algorithm -- 1.2.2.3 D* (D-star) Algorithm -- 1.2.2.4 Planning by STRIPS-Like Language -- 1.2.3 Single Agent RL -- 1.2.3.1 Multiarmed Bandit Problem -- 1.2.3.2 DP and Bellman Equation -- 1.2.3.3 Correlation Between RL and DP -- 1.2.3.4 Single Agent Q-Learning -- 1.2.3.5 Single Agent Planning Using Q-Learning -- 1.3 Multi-agent Planning and Coordination -- 1.3.1 Terminologies Related to Multi-agent Coordination -- 1.3.2 Classification of MAS -- 1.3.3 Game Theory for Multi-agent Coordination -- 1.3.3.1 Nash Equilibrium -- 1.3.3.2 Correlated Equilibrium -- 1.3.3.3 Static Game Examples -- 1.3.4 Correlation Among RL, DP, and GT -- 1.3.5 Classification of MARL -- 1.3.5.1 Cooperative MARL -- 1.3.5.2 Competitive MARL -- 1.3.5.3 Mixed MARL -- 1.3.6 Coordination and Planning by MAQL -- 1.3.7 Performance Analysis of MAQL and MAQL-Based Coordination -- 1.4 Coordination by Optimization Algorithm -- 1.4.1 PSO Algorithm -- 1.4.2 Firefly Algorithm -- 1.4.2.1 Initialization -- 1.4.2.2 Attraction to Brighter Fireflies -- 1.4.2.3 Movement of Fireflies -- 1.4.3 Imperialist Competitive Algorithm -- 1.4.3.1 Initialization -- 1.4.3.2 Selection of Imperialists and Colonies -- 1.4.3.3 Formation of Empires -- 1.4.3.4 Assimilation of Colonies -- 1.4.3.5 Revolution -- 1.4.3.6 Imperialistic Competition -- 1.4.4 Differential Evolution Algorithm -- 1.4.4.1 Initialization -- 1.4.4.2 Mutation -- 1.4.4.3 Recombination -- 1.4.4.4 Selection -- 1.4.5 Off-line Optimization 1.4.6 Performance Analysis of Optimization Algorithms -- 1.4.6.1 Friedman Test -- 1.4.6.2 Iman-Davenport Test -- 1.5 Summary -- References -- Chapter 2 Improve Convergence Speed of Multi-Agent Q-Learning for Cooperative Task Planning -- 2.1 Introduction -- 2.2 Literature Review -- 2.3 Preliminaries -- 2.3.1 Single Agent Q-learning -- 2.3.2 Multi-agent Q-learning -- 2.4 Proposed MAQL -- 2.4.1 Two Useful Properties -- 2.5 Proposed FCMQL Algorithms and Their Convergence Analysis -- 2.5.1 Proposed FCMQL Algorithms -- 2.5.2 Convergence Analysis of the Proposed FCMQL Algorithms -- 2.6 FCMQL-Based Cooperative Multi-agent Planning -- 2.7 Experiments and Results -- 2.8 Conclusions -- 2.9 Summary -- 2.A More Details on Experimental Results -- 2.A.1 Additional Details of Experiment 2.1 -- 2.A.2 Additional Details of Experiment 2.2 -- 2.A.3 Additional Details of Experiment 2.4 -- References -- Chapter 3 Consensus Q-Learning for Multi-agent Cooperative Planning -- 3.1 Introduction -- 3.2 Preliminaries -- 3.2.1 Single Agent Q-Learning -- 3.2.2 Equilibrium-Based Multi-agent Q-Learning -- 3.3 Consensus -- 3.4 Proposed CoQL and Planning -- 3.4.1 Consensus Q-Learning -- 3.4.2 Consensus-Based Multi-robot Planning -- 3.5 Experiments and Results -- 3.5.1 Experimental Setup -- 3.5.2 Experiments for CoQL -- 3.5.3 Experiments for Consensus-Based Planning -- 3.6 Conclusions -- 3.7 Summary -- References -- Chapter 4 An Efficient Computing of Correlated Equilibrium for Cooperative Q-Learning-Based Multi-Robot Planning -- 4.1 Introduction -- 4.2 Single-Agent Q-Learning and Equilibrium-Based MAQL -- 4.2.1 Single Agent Q-Learning -- 4.2.2 Equilibrium-Based MAQL -- 4.3 Proposed Cooperative MAQL and Planning -- 4.3.1 Proposed Schemes with Their Applicability -- 4.3.2 Immediate Rewards in Scheme-I and -II -- 4.3.3 Scheme-I-Induced MAQL -- 4.3.4 Scheme-II-Induced MAQL. 4.3.5 Algorithms for Scheme-I and II -- 4.3.6 Constraint OmegaQL-I/OmegaQL-II(COmegaQL-I/COmegaQL-II) -- 4.3.7 Convergence -- 4.3.8 Multi-agent Planning -- 4.4 Complexity Analysis -- 4.4.1 Complexity of CQL -- 4.4.1.1 Space Complexity -- 4.4.1.2 Time Complexity -- 4.4.2 Complexity of the Proposed Algorithms -- 4.4.2.1 Space Complexity -- 4.4.2.2 Time Complexity -- 4.4.3 Complexity Comparison -- 4.4.3.1 Space Complexity -- 4.4.3.2 Time Complexity -- 4.5 Simulation and Experimental Results -- 4.5.1 Experimental Platform -- 4.5.1.1 Simulation -- 4.5.1.2 Hardware -- 4.5.2 Experimental Approach -- 4.5.2.1 Learning Phase -- 4.5.2.2 Planning Phase -- 4.5.3 Experimental Results -- 4.6 Conclusion -- 4.7 Summary -- References -- Chapter 5 A Modified Imperialist Competitive Algorithm for Multi-Robot Stick-Carrying Application -- 5.1 Introduction -- 5.2 Problem Formulation for Multi-Robot Stick-Carrying -- 5.3 Proposed Hybrid Algorithm -- 5.3.1 An Overview of ICA -- 5.3.1.1 Initialization -- 5.3.1.2 Selection of Imperialists and Colonies -- 5.3.1.3 Formation of Empires -- 5.3.1.4 Assimilation of Colonies -- 5.3.1.5 Revolution -- 5.3.1.6 Imperialistic Competition -- 5.3.1.6.1 Total Empire Power Evaluation -- 5.3.1.6.2 Reassignment of Colonies and Removal of Empire -- 5.3.1.6.3 Union of Empires -- 5.4 An Overview of FA -- 5.4.1 Initialization -- 5.4.2 Attraction to Brighter Fireflies -- 5.4.3 Movement of Fireflies -- 5.5 Proposed ICFA -- 5.5.1 Assimilation of Colonies -- 5.5.1.1 Attraction to Powerful Colonies -- 5.5.1.2 Modification of Empire Behavior -- 5.5.1.3 Union of Empires -- 5.6 Simulation Results -- 5.6.1 Comparative Framework -- 5.6.2 Parameter Settings -- 5.6.3 Analysis on Explorative Power of ICFA -- 5.6.4 Comparison of Quality of the Final Solution -- 5.6.5 Performance Analysis -- 5.7 Computer Simulation and Experiment 5.7.1 Average Total Path Deviation (ATPD) -- 5.7.2 Average Uncovered Target Distance (AUTD) -- 5.7.3 Experimental Setup in Simulation Environment -- 5.7.4 Experimental Results in Simulation Environment -- 5.7.5 Experimental Setup with Khepera Robots -- 5.7.6 Experimental Results with Khepera Robots -- 5.8 Conclusion -- 5.9 Summary -- References -- Chapter 6 Conclusions and Future Directions -- 6.1 Conclusions -- 6.2 Future Directions -- Index -- EULA. Mehragentensystem (DE-588)4389058-1 gnd Bestärkendes Lernen Künstliche Intelligenz (DE-588)4825546-4 gnd
subject_GND	(DE-588)4389058-1 (DE-588)4825546-4
title	Multi-agent coordination a reinforcement learning approach
title_auth	Multi-agent coordination a reinforcement learning approach
title_exact_search	Multi-agent coordination a reinforcement learning approach
title_exact_search_txtP	Multi-agent coordination a reinforcement learning approach
title_full	Multi-agent coordination a reinforcement learning approach Arup Kumar Sadhu, Amit Konar
title_fullStr	Multi-agent coordination a reinforcement learning approach Arup Kumar Sadhu, Amit Konar
title_full_unstemmed	Multi-agent coordination a reinforcement learning approach Arup Kumar Sadhu, Amit Konar
title_short	Multi-agent coordination
title_sort	multi agent coordination a reinforcement learning approach
title_sub	a reinforcement learning approach
topic	Mehragentensystem (DE-588)4389058-1 gnd Bestärkendes Lernen Künstliche Intelligenz (DE-588)4825546-4 gnd
topic_facet	Mehragentensystem Bestärkendes Lernen Künstliche Intelligenz
work_keys_str_mv	AT sadhuarupkumar multiagentcoordinationareinforcementlearningapproach AT konaramit multiagentcoordinationareinforcementlearningapproach

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