{"product_id":"9798868802720","title":"Deep Reinforcement Learning with Python: RLHF for Chatbots and Large Language Models","description":"\u003ch1\u003eDeep Reinforcement Learning with Python: RLHF for Chatbots and Large Language Models\u003c\/h1\u003e \u003ch2\u003eSanghi, Nimish\u003c\/h2\u003e \u003cp\u003e\u003c\/p\u003e\u003cdiv\u003e\n\u003cp\u003eGain 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. \u003c\/p\u003e\n\u003cp\u003eNew 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.\u003c\/p\u003e\n\u003cp\u003eYou'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. \u003c\/p\u003e\n\u003cp\u003eWhether it’s for applications in gaming, robotics, or Generative AI, \u003ci\u003eDeep Reinforcement Learning with Python\u003c\/i\u003e will help keep you ahead of the curve.\u003c\/p\u003e\n\u003cdiv\u003e\u003cbr\u003e\u003c\/div\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003cb\u003eWhat You'll Learn\u003c\/b\u003e\u003cbr\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003cp\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eExplore      Python-based RL libraries, including StableBaselines3 and CleanRL  \u003c\/li\u003e\n\u003cli\u003eWork with diverse RL environments like Gymnasium, Pybullet, and Unity ML\u003c\/li\u003e\n\u003cli\u003eUnderstand instruction finetuning of Large Language Models using RLHF and PPO\u003c\/li\u003e\n\u003cli\u003eStudy training and optimization techniques using HuggingFace, Weights and Biases,      and Optuna \u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cul\u003e \u003c\/ul\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003cp\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eWho This Book Is For\u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003eSoftware engineers and machine learning developers eager to sharpen their understanding of deep RL and acquire practical skills in implementing RL algorithms fromscratch. \u003cbr\u003e\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e\u003cbr\u003e\u003c\/div\u003e \u003ch3\u003eDetails\u003c\/h3\u003e \u003cp\u003ePublished by: Apress\u003c\/p\u003e \u003cp\u003ePublication Date: 2024-07-15\u003c\/p\u003e \u003cp\u003eFormat: Paperback\u003c\/p\u003e \u003cp\u003eISBN-13: 9798868802720\u003c\/p\u003e \u003cp\u003eDOI: 10.1007\/979-8-8688-0273-7\u003c\/p\u003e \u003cp\u003eDimensions: 254cm x178cm\u003c\/p\u003e \u003cp\u003ePages: 634\u003c\/p\u003e ","brand":"Apress","offers":[{"title":"Default Title","offer_id":45385758245004,"sku":"9798868802720","price":53.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9798868802720.jpg?v=1776092756","url":"https:\/\/lateknightbooks.com\/products\/9798868802720","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}