LEADER 03713nam 22005175 450 001 9910874683203321 005 20240806114731.0 010 $a9798868802737$b(electronic bk.) 010 $z9798868802720 024 7 $a10.1007/979-8-8688-0273-7 035 $a(MiAaPQ)EBC31527438 035 $a(Au-PeEL)EBL31527438 035 $a(CKB)32825517000041 035 $a(OCoLC)1446222077 035 $a(OCoLC-P)1446222077 035 $a(CaSebORM)9798868802737 035 $a(DE-He213)979-8-8688-0273-7 035 $a(EXLCZ)9932825517000041 100 $a20240713d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDeep Reinforcement Learning with Python $eRLHF for Chatbots and Large Language Models /$fby Nimish Sanghi 205 $a2nd ed. 2024. 210 1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2024. 215 $a1 online resource (0 pages) 300 $aIncludes index. 311 08$aPrint version: Sanghi, Nimish Deep Reinforcement Learning with Python Berkeley, CA : Apress L. P.,c2024 9798868802720 327 $aChapter 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. 330 $aGain 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. 606 $aPython (Computer program language) 606 $aNatural language processing (Computer science) 606 $aArtificial intelligence$xComputer programs 615 0$aPython (Computer program language) 615 0$aNatural language processing (Computer science) 615 0$aArtificial intelligence$xComputer programs. 676 $a005.133 700 $aSanghi$b Nimish$01230146 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910874683203321 996 $aDeep reinforcement learning with Python$92855501 997 $aUNINA