Deep Reinforcement Learning with Python RLHF for Chatbots and Large Language Models
Gain a theoretical understanding of the most popular libraries in deep reinforcement learning (deep RL). This new edition focuses on the latest advances in deep RL using a learn-by-coding approach, allowing readers to assimilate and replicate the latest research in this field. New agent environments...
Other Authors: | |
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Format: | eBook |
Language: | Inglés |
Published: |
Berkeley, CA :
Apress
2024.
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Edition: | 2nd ed. 2024. |
Subjects: | |
See on Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009840469806719 |
Table of Contents:
- Chapter 1: Introduction to Reinforcement Learning
- Chapter 2: The Foundation – Markov Decision Processes
- Chapter 3: Model Based Approaches
- Chapter 4: Model Free Approaches
- Chapter 5: Function Approximation and Deep Reinforcement Learning
- Chapter 6: Deep Q-Learning (DQN)
- Chapter 7: Improvements to DQN
- Chapter 8: Policy Gradient Algorithms
- Chapter 9: Combining Policy Gradient and Q-Learning
- Chapter 10: Integrated Planning and Learning
- Chapter 11: Proximal Policy Optimization (PPO) and RLHF
- Chapter 12: Introduction to Multi Agent RL (MARL)
- Chapter 13: Additional Topics and Recent Advances.