The Art of Reinforcement Learning Fundamentals, Mathematics, and Implementations with Python
Unlock the full potential of reinforcement learning (RL), a crucial subfield of Artificial Intelligence, with this comprehensive guide. This book provides a deep dive into RL's core concepts, mathematics, and practical algorithms, helping you to develop a thorough understanding of this cutting-...
| Autor principal: | |
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Berkeley, CA :
Apress
2023.
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| Edición: | 1st ed. 2023. |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009786705406719 |
Tabla de Contenidos:
- Part I: Foundation
- Chapter 1: Introduction to Reinforcement Learning
- Chapter 2: Markov Decision Processes
- Chapter 3: Dynamic Programming
- Chapter 4: Monte Carlo Methods
- Chapter 5: Temporal Difference Learning
- Part II: Value Function Approximation
- Chapter 6: Linear Value Function Approximation
- Chapter 7: Nonlinear Value Function Approximation
- Chapter 8: Improvement to DQN
- Part III: Policy Approximation
- Chapter 9: Policy Gradient Methods
- Chapter 10: Problems with Continuous Action Space
- Chapter 11: Advanced Policy Gradient Methods
- Part IV: Advanced Topics
- Chapter 12: Distributed Reinforcement Learning
- Chapter 13: Curiosity-Driven Exploration
- Chapter 14: Planning with a Model – AlphaZero.
