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

✍ Scribed by Nimish Sanghi

• Publisher: Apress
• Year: 2024
• Tongue: English
• Leaves: 650
• Edition: 2
• Category: Library

✦ Synopsis

Gain a theoretical understanding to 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 covers how multiple agents compete, 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 by chatbots, built using Large Language Models, e.g. ChatGPT to improve conversational capabilities.

✦ Table of Contents

Cover
Front Matter
1. Introduction to Reinforcement Learning
2. The Foundation: Markov Decision Processes
3. Model-Based Approaches
4. Model-Free Approaches
5. Function Approximation and Deep Learning
6. Deep Q-Learning (DQN)
7. Improvements to DQN**
8. Policy Gradient Algorithms
9. Combining Policy Gradient and Q-Learning
10. Integrated Planning and Learning
11. Proximal Policy Optimization (PPO) and RLHF
12. Multi-Agent RL (MARL)
13. Additional Topics and Recent Advances
Back Matter