Multi-Agent Coordination: A Reinforcement Learning Approach (IEEE Press) - Image 1

£95.79

Wiley Multi-Agent Coordination: A Reinforcement Learning Approach (IEEE Press)

Name: Multi-Agent Coordination: A Reinforcement Learning Approach (IEEE Press)
Brand: Wiley
Price: 95.86 GBP
Availability: InStock

AI & Machine Learning

Price History & Forecast

Last 18 days • 18 data points (No recent data available)

Historical

Generating forecast...

Price Distribution

Price distribution over 18 days • 1 price levels

Days at Price

Price Analysis

Most common price: £96 (18 days, 100.0%)

Price range: £96 - £96

Price levels: 1 different prices over 18 days

Description

Discover the latest developments in multi-robot coordination techniques with this insightful and original resource Multi-Agent Coordination: A Reinforcement Learning Approach delivers a comprehensive, insightful, and unique treatment of the development of multi-robot coordination algorithms with minimal computational burden and reduced storage requirements when compared to traditional algorithms. The accomplished academics, engineers, and authors provide readers with both a high-level introduction to, and overview of, multi-robot coordination, and in-depth analyses of learning-based planning algorithms. You'll learn about how to accelerate the exploration of the team-goal and alternative approaches to speeding up the convergence of TMAQL by identifying the preferred joint action for the team. The authors also propose novel approaches to consensus Q-learning that address the equilibrium selection problem and a new way of evaluating the threshold value for uniting empires without imposing any significant computation overhead. Finally, the book concludes with an examination of the likely direction of future research in this rapidly developing field. Readers will discover cutting-edge techniques for multi-agent coordination, including: An introduction to multi-agent coordination by reinforcement learning and evolutionary algorithms, including topics like the Nash equilibrium and correlated equilibrium Improving convergence speed of multi-agent Q-learning for cooperative task planning Consensus Q-learning for multi-agent cooperative planning The efficient computing of correlated equilibrium for cooperative q-learning based multi-agent planning A modified imperialist competitive algorithm for multi-agent stick-carrying applications Perfect for academics, engineers, and professionals who regularly work with multi-agent learning algorithms, Multi-Agent Coordination: A Reinforcement Learning Approach also belongs on the bookshelves of anyone with an advanced interest in machine learning and artificial intelligence as it applies to the field of cooperative or competitive robotics.

Product Specifications

Brand: Wiley
Format: Hardcover
ASIN: 1119699037
Category: Books > Subjects > Computing & Internet > Computer Science > AI & Machine Learning
Domain: Amazon UK
Publication Date: 20 November 2020
Listed Since: 16 March 2020

Barcode

No barcode data available

Wiley Multi-Agent Coordination: A Reinforcement Learning Approach (IEEE Press)

Price History & Forecast

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

Human-Robot Interaction Control Using Reinforcement Learning (IEEE Press Series on Systems Science and Engineering)

CRC Press Multiagent Robotic Systems - Computational Intelligence

Multiagent Systems: Introduction and Coordination Control

Multi-Agent Reinforcement Learning: Foundations and Modern Approaches

Reinforcement Learning for Sequential Decision and Optimal Control

Reinforcement Learning for Decision & Optimal Control - Springer

Distributed Cooperative Control: Emerging Applications

Learning for Adaptive and Reactive Robot Control: A Dynamical Systems Approach (Intelligent Robotics and Autonomous Agents series)

Multiagent Systems: Introduction and Coordination Control

Distributed Coordination of Multi-agent Networks: Emergent Problems, Models, and Issues (Communications and Control Engineering)

Model-based Reinforcement Learning: A Survey (Foundations and Trends® in Machine Learning)

Emergent Behavior Detection and Task Coordination for Multiagent Systems: A Distributed Estimation and Control Approach: 397 (Studies in Systems, Decision and Control, 397)

Team Cooperation in a Network of Multi-Vehicle Unmanned Systems: Synthesis of Consensus Algorithms

Advanced Distributed Consensus for Multiagent Systems

Multi-Arm Cooperating Robots: Dynamics and Control: 30 (Intelligent Systems, Control and Automation: Science and Engineering, 30)

Reinforcement Learning for Cyber Operations: Applications of Artificial Intelligence for Penetration Testing

Reinforcement Learning and Dynamic Programming Using Function Approximators: 39 (Automation and Control Engineering)

Approaches to Probabilistic Model Learning for Mobile Manipulation Robots: 89 (Springer Tracts in Advanced Robotics, 89)

Formation Control of Multi-Agent Systems: A Graph Rigidity Approach (Wiley Series in Dynamics and Control of Electromechanical Systems)

Team Cooperation in a Network of Multi-Vehicle Unmanned Systems: Synthesis of Consensus Algorithms

Learning in Cooperative Multi-Agent Systems: Distributed Reinforcement Learning Algorithms and their Application to Scheduling Problems

Robot Intelligence: An Advanced Knowledge Processing Approach (Advanced Information and Knowledge Processing)

Learning Automata and Their Applications to Intelligent Systems

Reinforcement and Systemic Machine Learning for Decision Making (IEEE Press Series on Systems Science and Engineering)