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Deep Reinforcement Learning with Python RLHF for Chatbots and Large Language Models
Author
Title
Deep Reinforcement Learning with Python [electronic resource] : RLHF for Chatbots and Large Language Models / by Nimish Sanghi.
ISBN
9798868802737
Edition
2nd ed. 2024.
Publication
Berkeley, CA : Apress : Imprint: Apress, 2024.
Physical Description
1 online resource (XXV, 634 p.) 204 illus.
Local Notes
Access is available to the Yale community.
Access and use
Access restricted by licensing agreement.
Summary
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 ranging from games, and robotics to finance are explained to help you try different ways to apply reinforcement learning. A chapter on multi-agent reinforcement learning (MARL) covers how multiple agents can be trained, while another chapter focuses on the widely used deep RL algorithm, proximal policy optimization (PPO). You'll see how reinforcement learning with human feedback (RLHF) has been used to fine-tune Large Language Models (LLMs) to chat and follow instructions. An example of this is the OpenAI ChatGPT offering human like conversational capabilities. You'll also review the steps for using the code on multiple cloud systems and deploying models on platforms such as Hugging Face Hub. The code is in Jupyter Notebook, which can be run on Google Colab, and other similar deep learning cloud platforms, allowing you to tailor the code to your own needs. Whether it's for applications in gaming, robotics, or Generative AI, Deep Reinforcement Learning with Python will help keep you ahead of the curve.
Variant and related titles
Springer ENIN.
Other formats
Printed edition:
Printed edition:
Printed edition:
Format
Books / Online
Language
English
Added to Catalog
August 07, 2024
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.
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