Multi-agent machine learning: a reinforcement approach
"Multi-Agent Machine Learning: A Reinforcement Learning Approach is a framework to understanding different methods and approaches in multi-agent machine learning. It also provides cohesive coverage of the latest advances in multi-agent differential games and presents applications in game theory...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Hoboken, New Jersey
Wiley
2014
|
| Schlagworte: | |
| Online-Zugang: | Cover image Inhaltsverzeichnis |
| Zusammenfassung: | "Multi-Agent Machine Learning: A Reinforcement Learning Approach is a framework to understanding different methods and approaches in multi-agent machine learning. It also provides cohesive coverage of the latest advances in multi-agent differential games and presents applications in game theory and robotics. Framework for understanding a variety of methods and approaches in multi-agent machine learning. Discusses methods of reinforcement learning such as a number of forms of multi-agent Q-learning Applicable to research professors and graduate students studying electrical and computer engineering, computer science, and mechanical and aerospace engineering".. |
| Beschreibung: | xi, 242 Seiten Diagramme |
| ISBN: | 9781118362082 |
Internformat
MARC
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| 650 | 4 | |a Reinforcement learning | |
| 650 | 4 | |a Differential games | |
| 650 | 4 | |a Swarm intelligence | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a TECHNOLOGY & ENGINEERING / Electronics / General | |
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Datensatz im Suchindex
| _version_ | 1804152699155906560 |
|---|---|
| adam_text | Titel: Multi-agent machine learning
Autor: Schwartz, Howard M
Jahr: 2014
Contents
Preface ix
Chapter 1 A Brief Review of Supervised Learning 1
1.1 Least Squares Estimates 1
1.2 Recursive Least Squares 5
1.3 Least Mean Squares 6
1.4 Stochastic Approximation 10
References 11
Chapter 2 Single-Agent Reinforcement Learning 12
2.1 Introduction 12
2.2 n-Armed Bandit Problem 13
2.3 The Learning Structure 15
2.4 The Value Function 17
2.5 The Optimal Value Functions 18
2.5.1 The Grid World Example 20
2.6 Markov Decision Processes 23
2.7 Learning Value Functions 25
2.8 Policy Iteration 26
2.9 Temporal Difference Learning 28
2.10 TD Learning of the State-Action Function 30
Vl Contents
2.11 Q-Learning 32
2.12 Eligibility Traces 33
References 37
Chapter 3 Learning in Two-Player Matrix Games 38
3.1 Matrix Games 38
3.2 Nash Equilibria in Two-Player Matrix Games 42
3.3 Linear Programming in Two-Player Zero-Sum Matrix
Games 43
3.4 The Learning Algorithms 47
3.5 Gradient Ascent Algorithm 47
3.6 WoLF-IGA Algorithm 51
3.7 Policy Hill Climbing (PHC) 52
3.8 WoLF-PHC Algorithm 54
3.9 Decentralized Learning in Matrix Games 57
3.10 Learning Automata 59
3.11 Linear Reward-Inaction Algorithm 59
3.12 Linear Reward-Penalty Algorithm 60
3.13 The Lagging Anchor Algorithm 60
3.14 Lr_, Lagging Anchor Algorithm 62
3.14.1 Simulation 68
References 70
Chapter 4 Learning in Multiplayer Stochastic Games 73
4.1 Introduction 73
4.2 Multiplayer Stochastic Games 75
4.3 Minimax-Q Algorithm 79
4.3.1 2x2 Grid Game 80
4.4 Nash Q-Learning 87
4.4.1 The Learning Process 95
4.5 The Simplex Algorithm 96
4.6 The Lemke-Howson Algorithm 100
4.7 Nash-Q Implementation 107
4.8 Friend-or-Foe Q-Learning 111
4.9 Infinite Gradient Ascent 112
Contents vii
4.10 Policy Hill Climbing 114
4.11 WoLF-PHC Algorithm 114
4.12 Guarding a Territory Problem in a Grid World 117
4.12.1 Simulation and Results 119
4.13 Extension of Ln_, Lagging Anchor Algorithm to
Stochastic Games 125
4.14 The Exponential Moving-Average Q-Learning
(EMA Q-Learning) Algorithm 128
4.15 Simulation and Results Comparing EMA
Q-Learning to Other Methods 131
4.15.1 Matrix Games 131
4.15.2 Stochastic Games 134
References 141
Chapter 5 Differential Games 144
5.1 Introduction 144
5.2 A Brief Tutorial on Fuzzy Systems 146
5.2.1 Fuzzy Sets and Fuzzy Rules 146
5.2.2 Fuzzy Inference Engine 148
5.2.3 Fuzzifier and Defuzzifier 151
5.2.4 Fuzzy Systems and Examples 152
5.3 Fuzzy Q-Learning 155
5.4 Fuzzy Actor-Critic Learning 159
5.5 Homicidal Chauffeur Differential Game 162
5.6 Fuzzy Controller Structure 165
5.7 Q(A)-Learning Fuzzy Inference System 166
5.8 Simulation Results for the Homicidal Chauffeur — 171
5.9 Learning in the Evader-Pursuer Game with Two
Cars 174
5.10 Simulation of the Game of Two Cars 177
5.11 Differential Game of Guarding a Territory 180
5.12 Reward Shaping in the Differential Game
of Guarding a Territory 184
5.13 Simulation Results 185
5.13.1 One Defender Versus One Invader 185
5.13.2 Two Defenders Versus One Invader 191
References 197
viii Contents
Chapter 6 Swarm Intelligence and the Evolution
of Personality Traits 200
6.1 Introduction 200
6.2 The Evolution of Swarm Intelligence 200
6.3 Representation of the Environment 201
6.4 Swarm-Based Robotics in Terms
of Personalities 203
6.5 Evolution of Personality Traits 206
6.6 Simulation Framework 207
6.7 A Zero-Sum Game Example 208
6.7.1 Convergence 208
6.7.2 Simulation Results 214
6.8 Implementation for Next Sections 216
6.9 Robots Leaving a Room 218
6.10 Tracking a Target 221
6.11 Conclusion 232
References 233
Index -237
|
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| isbn | 9781118362082 |
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| physical | xi, 242 Seiten Diagramme |
| publishDate | 2014 |
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| spelling | Schwartz, Howard M. Verfasser (DE-588)1059786249 aut Multi-agent machine learning a reinforcement approach Howard M. Schwartz, Department of Systems and Computer Engineering Carleton University Hoboken, New Jersey Wiley 2014 xi, 242 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier "Multi-Agent Machine Learning: A Reinforcement Learning Approach is a framework to understanding different methods and approaches in multi-agent machine learning. It also provides cohesive coverage of the latest advances in multi-agent differential games and presents applications in game theory and robotics. Framework for understanding a variety of methods and approaches in multi-agent machine learning. Discusses methods of reinforcement learning such as a number of forms of multi-agent Q-learning Applicable to research professors and graduate students studying electrical and computer engineering, computer science, and mechanical and aerospace engineering".. TECHNOLOGY & ENGINEERING / Electronics / General bisacsh Reinforcement learning Differential games Swarm intelligence Machine learning TECHNOLOGY & ENGINEERING / Electronics / General Mehragentensystem (DE-588)4389058-1 gnd rswk-swf Bestärkendes Lernen Künstliche Intelligenz (DE-588)4825546-4 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Schwarmintelligenz (DE-588)4793676-9 gnd rswk-swf Mehragentensystem (DE-588)4389058-1 s Maschinelles Lernen (DE-588)4193754-5 s DE-604 Bestärkendes Lernen Künstliche Intelligenz (DE-588)4825546-4 s Schwarmintelligenz (DE-588)4793676-9 s 1\p DE-604 http://catalogimages.wiley.com/images/db/jimages/9781118362082.jpg Cover image HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027626064&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Schwartz, Howard M. Multi-agent machine learning a reinforcement approach TECHNOLOGY & ENGINEERING / Electronics / General bisacsh Reinforcement learning Differential games Swarm intelligence Machine learning TECHNOLOGY & ENGINEERING / Electronics / General Mehragentensystem (DE-588)4389058-1 gnd Bestärkendes Lernen Künstliche Intelligenz (DE-588)4825546-4 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Schwarmintelligenz (DE-588)4793676-9 gnd |
| subject_GND | (DE-588)4389058-1 (DE-588)4825546-4 (DE-588)4193754-5 (DE-588)4793676-9 |
| title | Multi-agent machine learning a reinforcement approach |
| title_auth | Multi-agent machine learning a reinforcement approach |
| title_exact_search | Multi-agent machine learning a reinforcement approach |
| title_full | Multi-agent machine learning a reinforcement approach Howard M. Schwartz, Department of Systems and Computer Engineering Carleton University |
| title_fullStr | Multi-agent machine learning a reinforcement approach Howard M. Schwartz, Department of Systems and Computer Engineering Carleton University |
| title_full_unstemmed | Multi-agent machine learning a reinforcement approach Howard M. Schwartz, Department of Systems and Computer Engineering Carleton University |
| title_short | Multi-agent machine learning |
| title_sort | multi agent machine learning a reinforcement approach |
| title_sub | a reinforcement approach |
| topic | TECHNOLOGY & ENGINEERING / Electronics / General bisacsh Reinforcement learning Differential games Swarm intelligence Machine learning TECHNOLOGY & ENGINEERING / Electronics / General Mehragentensystem (DE-588)4389058-1 gnd Bestärkendes Lernen Künstliche Intelligenz (DE-588)4825546-4 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Schwarmintelligenz (DE-588)4793676-9 gnd |
| topic_facet | TECHNOLOGY & ENGINEERING / Electronics / General Reinforcement learning Differential games Swarm intelligence Machine learning Mehragentensystem Bestärkendes Lernen Künstliche Intelligenz Maschinelles Lernen Schwarmintelligenz |
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