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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...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken, NJ :
John Wiley & Sons,
[2014]
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Schwartz, Howard M., |e editor. | |
| 245 | 1 | 0 | |a Multi-agent machine learning : |b a reinforcement approach / |c Howard M. Schwartz. |
| 264 | 1 | |a Hoboken, NJ : |b John Wiley & Sons, |c [2014] | |
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 504 | |a Includes bibliographical references and index. | ||
| 520 | |a "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"-- |c Provided by publisher | ||
| 520 | |a "Provide an in-depth coverage of multi-player, differential games and Gam theory"-- |c Provided by publisher | ||
| 588 | 0 | |a Print version record and CIP data provided by publisher. | |
| 505 | 0 | |a Cover; Title Page; Copyright; Preface; References; Chapter 1: A Brief Review of Supervised Learning; 1.1 Least Squares Estimates; 1.2 Recursive Least Squares; 1.3 Least Mean Squares; 1.4 Stochastic Approximation; References; Chapter 2: Single-Agent Reinforcement Learning; 2.1 Introduction; 2.2 n-Armed Bandit Problem; 2.3 The Learning Structure; 2.4 The Value Function; 2.5 The Optimal Value Functions; 2.6 Markov Decision Processes; 2.7 Learning Value Functions; 2.8 Policy Iteration; 2.9 Temporal Difference Learning; 2.10 TD Learning of the State-Action Function; 2.11 Q-Learning. | |
| 505 | 8 | |a 2.12 Eligibility TracesReferences; Chapter 3: Learning in Two-Player Matrix Games; 3.1 Matrix Games; 3.2 Nash Equilibria in Two-Player Matrix Games; 3.3 Linear Programming in Two-Player Zero-Sum Matrix Games; 3.4 The Learning Algorithms; 3.5 Gradient Ascent Algorithm; 3.6 WoLF-IGA Algorithm; 3.7 Policy Hill Climbing (PHC); 3.8 WoLF-PHC Algorithm; 3.9 Decentralized Learning in Matrix Games; 3.10 Learning Automata; 3.11 Linear Reward-Inaction Algorithm; 3.12 Linear Reward-Penalty Algorithm; 3.13 The Lagging Anchor Algorithm; 3.14 L R-I Lagging Anchor Algorithm; References. | |
| 505 | 8 | |a Chapter 4: Learning in Multiplayer Stochastic Games4.1 Introduction; 4.2 Multiplayer Stochastic Games; 4.3 Minimax-Q Algorithm; 4.4 Nash Q-Learning; 4.5 The Simplex Algorithm; 4.6 The Lemke-Howson Algorithm; 4.7 Nash-Q Implementation; 4.8 Friend-or-Foe Q-Learning; 4.9 Infinite Gradient Ascent; 4.10 Policy Hill Climbing; 4.11 WoLF-PHC Algorithm; 4.12 Guarding a Territory Problem in a Grid World; 4.13 Extension of L R-I Lagging Anchor Algorithm to Stochastic Games; 4.14 The Exponential Moving-Average Q-Learning (EMA Q-Learning) Algorithm. | |
| 505 | 8 | |a 5.12 Reward Shaping in the Differential Game of Guarding a Territory5.13 Simulation Results; References; Chapter 6: Swarm Intelligence and the Evolution of Personality Traits; 6.1 Introduction; 6.2 The Evolution of Swarm Intelligence; 6.3 Representation of the Environment; 6.4 Swarm-Based Robotics in Terms of Personalities; 6.5 Evolution of Personality Traits; 6.6 Simulation Framework; 6.7 A Zero-Sum Game Example; 6.8 Implementation for Next Sections; 6.9 Robots Leaving a Room; 6.10 Tracking a Target; 6.11 Conclusion; References; Index; End User License Agreement. | |
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Reinforcement learning. | |
| 650 | 0 | |a Differential games. | |
| 650 | 0 | |a Swarm intelligence. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 6 | |a Apprentissage par renforcement (Intelligence artificielle) | |
| 650 | 6 | |a Jeux différentiels. | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 7 | |a TECHNOLOGY & ENGINEERING |x Electronics |x General. |2 bisacsh | |
| 650 | 7 | |a Differential games |2 fast | |
| 650 | 7 | |a Machine learning |2 fast | |
| 650 | 7 | |a Reinforcement learning |2 fast | |
| 650 | 7 | |a Swarm intelligence |2 fast | |
| 776 | 0 | 8 | |i Print version: |a Schwartz, Howard M. |t Multi-agent machine learning. |d Hoboken, NJ : John Wiley & Sons, [2014] |z 9781118362082 |w (DLC) 2014016950 |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781118362082/?ar |z Texto completo (Requiere registro previo con correo institucional) |
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