Reinforcement Learning Theory and Python Implementation /

Reinforcement Learning: Theory and Python Implementation is a tutorial book on reinforcement learning, with explanations of both theory and applications. Starting from a uniform mathematical framework, this book derives the theory of modern reinforcement learning systematically and introduces all ma...

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Hlavní autor:	Xiao, Zhiqing (Autor)
Korporativní autor:	SpringerLink (Online service)
Médium:	Elektronický zdroj E-kniha
Jazyk:	angličtina
Vydáno:	Singapore : Springer Nature Singapore : Imprint: Springer, 2024.
Vydání:	1st ed. 2024.
Témata:	Machine learning. Artificial intelligence. Robotics. Machine Learning. Artificial Intelligence.
On-line přístup:	https://doi.org/10.1007/978-981-19-4933-3
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

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Chapter 1. Introduction of Reinforcement Learning (RL)
Chapter 2. MDP: Markov Decision Process
Chapter 3. Model-based Numerical Iteration
Chapter 4. MC: Monte Carlo Learning
Chapter 5. TD: Temporal Difference Learning
Chapter 6. Function Approximation
Chapter 7. PG: Policy Gradient
Chapter 8. AC: Actor–Critic
Chapter 9. DPG: Deterministic Policy Gradient
Chapter 10. Maximum-Entropy RL
Chapter 11. Policy-based Gradient-Free Algorithms
Chapter 12. Distributional RL
Chapter 13. Minimize Regret
Chapter 14. Tree Search
Chapter 15. More Agent–Environment Interfaces
Chapter 16. Learn from Feedback and Imitation Learning.

Reinforcement Learning Theory and Python Implementation /

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