Deep Reinforcement Learning with Python : RLHF for Chatbots and Large Language Models / by Nimish Sanghi.

Format:

Book

Author/Creator:

Sanghi, Nimish, author.

Language:

English

Subjects (All):

Machine learning.

Artificial intelligence.

Python (Computer program language).

Machine Learning.

Artificial Intelligence.

Python.

Local Subjects:

Machine Learning.

Artificial Intelligence.

Python.

Physical Description:

1 online resource (0 pages)

Edition:

2nd ed. 2024.

Place of Publication:

Berkeley, CA : Apress : Imprint: Apress, 2024.

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.

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.

Notes:

Includes index.

Other Format:

Print version: Sanghi, Nimish Deep Reinforcement Learning with Python

ISBN:

9798868802737

OCLC:

1446222077