Reinforcement Learning
- Indbinding:
- Hardback
- Sideantal:
- 328
- Udgivet:
- 25. juli 2023
- Udgave:
- 23001
- Størrelse:
- 160x23x241 mm.
- Vægt:
- 718 g.
- Ukendt - mangler pt..
Normalpris
Abonnementspris
- Rabat på køb af fysiske bøger
- 1 valgfrit digitalt ugeblad
- 20 timers lytning og læsning
- Adgang til 70.000+ titler
- Ingen binding
Abonnementet koster 75 kr./md.
Ingen binding og kan opsiges når som helst.
- 1 valgfrit digitalt ugeblad
- 20 timers lytning og læsning
- Adgang til 70.000+ titler
- Ingen binding
Abonnementet koster 75 kr./md.
Ingen binding og kan opsiges når som helst.
Beskrivelse af Reinforcement Learning
This book offers a thorough introduction to the basics and scientific and technological innovations involved in the modern study of reinforcement-learning-based feedback control. The authors address a wide variety of systems including work on nonlinear, networked, multi-agent and multi-player systems.
A concise description of classical reinforcement learning (RL), the basics of optimal control with dynamic programming and network control architectures, and a brief introduction to typical algorithms build the foundation for the remainder of the book. Extensive research on data-driven robust control for nonlinear systems with unknown dynamics and multi-player systems follows. Data-driven optimal control of networked single- and multi-player systems leads readers into the development of novel RL algorithms with increased learning efficiency. The book concludes with a treatment of how these RL algorithms can achieve optimal synchronization policies for multi-agentsystems with unknown model parameters and how game RL can solve problems of optimal operation in various process industries. Illustrative numerical examples and complex process control applications emphasize the realistic usefulness of the algorithms discussed.
The combination of practical algorithms, theoretical analysis and comprehensive examples presented in Reinforcement Learning will interest researchers and practitioners studying or using optimal and adaptive control, machine learning, artificial intelligence, and operations research, whether advancing the theory or applying it in mineral-process, chemical-process, power-supply or other industries.
A concise description of classical reinforcement learning (RL), the basics of optimal control with dynamic programming and network control architectures, and a brief introduction to typical algorithms build the foundation for the remainder of the book. Extensive research on data-driven robust control for nonlinear systems with unknown dynamics and multi-player systems follows. Data-driven optimal control of networked single- and multi-player systems leads readers into the development of novel RL algorithms with increased learning efficiency. The book concludes with a treatment of how these RL algorithms can achieve optimal synchronization policies for multi-agentsystems with unknown model parameters and how game RL can solve problems of optimal operation in various process industries. Illustrative numerical examples and complex process control applications emphasize the realistic usefulness of the algorithms discussed.
The combination of practical algorithms, theoretical analysis and comprehensive examples presented in Reinforcement Learning will interest researchers and practitioners studying or using optimal and adaptive control, machine learning, artificial intelligence, and operations research, whether advancing the theory or applying it in mineral-process, chemical-process, power-supply or other industries.
Brugerbedømmelser af Reinforcement Learning
Giv din bedømmelse
For at bedømme denne bog, skal du være logget ind.Andre købte også..
Find lignende bøger
Bogen Reinforcement Learning findes i følgende kategorier:
- Business og læring > Computer og IT
- Reference, information og tværfaglige emner > Forskning og information: generelt > Informationsteori > Kybernetik og systemteori
- Teknologi, ingeniørvidenskab og landbrug > Teknologi: generelle emner > Matematik for ingeniører
- Teknologi, ingeniørvidenskab og landbrug > Industriel kemi og produktionsteknologi > Industriel kemi og kemiteknik > Procesteknologi
- Teknologi, ingeniørvidenskab og landbrug > Maskinteknik og materialer > Produktionsteknik
- Teknologi, ingeniørvidenskab og landbrug > Elektronik og kommunikationsteknik > Elektronik teknik > Automatisk styringsteknik og reguleringsteknik
- Databehandling og informationsteknologi > Informatik > Kunstig intelligens
© 2024 Pling BØGER Registered company number: DK43351621