Multi-agent machine learning: a reinforcement approach

✍ Scribed by Schwartz, Howard M

• Publisher: John Wiley & Sons
• Year: 2014
• Tongue: English
• Category: Library

✦ Synopsis

"Provide an in-depth coverage of multi-player, differential games and Gam theory"--;"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"--;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.

✦ Table of Contents

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. 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. 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. 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.

✦ Subjects

Differential games;Machine learning;Reinforcement learning;Swarm intelligence;TECHNOLOGY & ENGINEERING--Electronics--General;Electronic books;TECHNOLOGY & ENGINEERING -- Electronics -- General