Deep Reinforcement Learning with Python : RLHF for Chatbots and Large Language Models

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...

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Detalles Bibliográficos
Otros Autores: Sanghi, Nimish, author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Berkeley, CA : Apress 2024.
Edición:2nd ed. 2024.
Materias:
ChatGPT.
Python (Computer program language)
Natural language processing (Computer science)
Artificial intelligence > Computer programs.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009840469806719
Tabla de Contenidos:
  • 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.

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