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Mathematical Foundations of Reinforcement Learning

This book provides a mathematical yet accessible introduction to the fundamental concepts, core challenges, and classic reinforcement learning algorithms. It aims to help readers understand the theoretical foundations of algorithms, providing insights into their design and functionality. Numerous il...

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Dettagli Bibliografici
Autore principale: Zhao, Shiyu (Autore)
Ente Autore: SpringerLink (Online service)
Natura: Elettronico eBook
Lingua:inglese
Pubblicazione: Singapore : Springer Nature Singapore : Imprint: Springer, 2025.
Edizione:1st ed. 2025.
Soggetti:
Artificial intelligence.
Machine learning.
Artificial intelligence > Data processing.
Multiagent systems.
Artificial Intelligence.
Machine Learning.
Data Science.
Multiagent Systems.
Accesso online:https://doi.org/10.1007/978-981-97-3944-8
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Sommario:
  • 1 Basic Concepts
  • 2 State Value and Bellman Equation
  • 3 Optimal State Value and Bellman Optimality Equation
  • 4 Value Iteration and Policy Iteration
  • 5 Monte Carlo Learning
  • 6 Stochastic Approximation
  • 7 Temporal-Difference Learning
  • 8 Value Function Approximation
  • 9 Policy Gradient
  • 10 Actor-Critic Methods.

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