Book description
The book begins with a chapter on traditional methods of supervised learning, covering recursive least squares learning, mean square error methods, and stochastic approximation. Chapter 2 covers single agent reinforcement learning. Topics include learning value functions, Markov games, and TD learning with eligibility traces. Chapter 3 discusses two player games including two player matrix games with both pure and mixed strategies. Numerous algorithms and examples are presented. Chapter 4 covers learning in multiplayer games, stochastic games, and Markov games, focusing on learning multiplayer grid games—two player grid games, Qlearning, and Nash Qlearning. Chapter 5 discusses differential games, including multi player differential games, actor critique structure, adaptive fuzzy control and fuzzy interference systems, the evader pursuit game, and the defending a territory games. Chapter 6 discusses new ideas on learning within robotic swarms and the innovative idea of the evolution of personality traits.
• Framework for understanding a variety of methods and approaches in multiagent machine learning.
• Discusses methods of reinforcement learning such as a number of forms of multiagent Qlearning
• Applicable to research professors and graduate students studying electrical and computer engineering, computer science, and mechanical and aerospace engineering
Table of contents
 Cover
 Title
 Copyright
 Preface
 Chapter 1: A Brief Review of Supervised Learning

Chapter 2: SingleAgent Reinforcement Learning
 2.1 Introduction
 2.2 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 StateAction Function
 2.11 QLearning
 2.12 Eligibility Traces
 References

Chapter 3: Learning in TwoPlayer Matrix Games
 3.1 Matrix Games
 3.2 Nash Equilibria in TwoPlayer Matrix Games
 3.3 Linear Programming in TwoPlayer ZeroSum Matrix Games
 3.4 The Learning Algorithms
 3.5 Gradient Ascent Algorithm
 3.6 WoLFIGA Algorithm
 3.7 Policy Hill Climbing (PHC)
 3.8 WoLFPHC 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 Lagging Anchor Algorithm
 References

Chapter 4: Learning in Multiplayer Stochastic Games
 4.1 Introduction
 4.2 Multiplayer Stochastic Games
 4.3 MinimaxQ Algorithm
 4.3 MinimaxQ Algorithm
 4.5 The Simplex Algorithm
 4.6 The Lemke–Howson Algorithm
 4.7 NashQ Implementation
 4.8 FriendorFoe QLearning
 4.9 Infinite Gradient Ascent
 4.10 Policy Hill Climbing
 4.11 WoLFPHC Algorithm
 4.12 Guarding a Territory Problem in a Grid World
 4.13 Extension of Lagging Anchor Algorithm to Stochastic Games
 4.14 The Exponential MovingAverage QLearning (EMA QLearning) Algorithm
 4.15 Simulation and Results Comparing EMA QLearning to Other Methods
 References

Chapter 5: Differential Games
 5.1 Introduction
 5.2 A Brief Tutorial on Fuzzy Systems
 5.3 Fuzzy QLearning
 5.4 Fuzzy Actor–Critic Learning
 5.5 Homicidal Chauffeur Differential Game
 5.6 Fuzzy Controller Structure
 5.7 Q()Learning Fuzzy Inference System
 5.9 Learning in the Evader–Pursuer Game with Two Cars
 5.6 Fuzzy Controller Structure
 5.10 Simulation of the Game of Two Cars
 5.11 Differential Game of Guarding a Territory
 5.12 Reward Shaping in the Differential Game of Guarding a Territory
 5.13 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 SwarmBased Robotics in Terms of Personalities
 6.5 Evolution of Personality Traits
 6.6 Simulation Framework
 6.7 A ZeroSum 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
Product information
 Title: MultiAgent Machine Learning
 Author(s):
 Release date: August 2014
 Publisher(s): Wiley
 ISBN: 9781118362082
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