Deep reinforcement learning with Python

• Verfasst von: Sanghi, Nimish [VerfasserIn]
• Titel: Deep reinforcement learning with Python
• Titelzusatz: RLHF for chatbots and large language models
• Verf.angabe: Nimish Sanghi
• Ausgabe: Second edition.
• Verlagsort: New York, NY
• Verlag: Apress
• E-Jahr: 2024
• Jahr: [2024]
• Umfang: 1 online resource (650 pages)
• Illustrationen: illustrations
• Fussnoten: Includes bibliographical references and index
• ISBN: 9798868802737
• Datenträger: Online-Ressource
• Sprache: eng
• Bibliogr. Hinweis: Erscheint auch als : Druck-Ausgabe
• Sach-SW: Python (Langage de programmation)
• Sach-SW: Traitement automatique des langues naturelles
• Sach-SW: Intelligence artificielle ; Logiciels
• K10plus-PPN: 1899108009
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m4568063094
• Lokale URL Inst.: Zum Volltext

Abstract

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. 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 canbe 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.