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Deep Reinforcement Learning with Python

Deep Reinforcement Learning with Python

von Nimish Sanghi
Softcover - 9798868802720
64,19 €
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Beschreibung

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.What You'll LearnExplore Python-based RL libraries, including StableBaselines3 and CleanRL  Work with diverse RL environments like Gymnasium, Pybullet, and Unity MLUnderstand instruction finetuning of Large Language Models using RLHF and PPOStudy training and optimization techniques using HuggingFace, Weights and Biases,      and Optuna  Who This Book Is ForSoftware engineers and machine learning developers eager to sharpen their understanding of deep RL and acquire practical skills in implementing RL algorithms fromscratch. 

RLHF for Chatbots and Large Language Models

Details

Verlag APRESS
Ersterscheinung 15. Juli 2024
Maße 25.4 cm x 17.8 cm
Gewicht 1221 Gramm
Format Softcover
ISBN-13 9798868802720
Auflage Second Edition
Seiten 634