000			04408nam a22003017a 4500
003			OSt
005			20260520150758.0
008			230801b |||||||| |||| 00| 0 eng d
020			_a9781835882702
020			_a1835882706
041			_aeng
082			_a006.31 LAP-D
100			_aLapan, Maxim _930855
245			_aDeep reinforcement learning hands-on. _bA practical and easy-to-follow guide to RL from Q-Learning and DQNs to PPO and RLHF
250			_a3rd
260			_aBrimingham, UK _bPackt Publishing _c2024
300			_axxviii, 684p.
500			_aMaxim Lapan delivers intuitive explanations and insights into complex reinforcement learning (RL) concepts, starting from the basics of RL on simple environments and tasks to modern, state-of-the-art methods Purchase of the print or Kindle book includes a free PDF eBook Free with your book: DRM-free PDF version + access to Packt's next-gen Reader* Key Features: - Learn with concise explanations, modern libraries, and diverse applications from games to stock trading and web navigation - Develop deep RL models, improve their stability, and efficiently solve complex environments - New content on RL from human feedback (RLHF), MuZero, and transformers
500			_aWho this book is for: This book is ideal for machine learning engineers, software engineers, and data scientists looking to learn and apply deep reinforcement learning in practice. It assumes familiarity with Python, calculus, and machine learning concepts. With practical examples and high-level overviews, it's also suitable for experienced professionals looking to deepen their understanding of advanced deep RL methods and apply them across industries, such as gaming and finance
505			_aTable of Contents - What Is Reinforcement Learning? - OpenAI Gym - Deep Learning with PyTorch - The Cross-Entropy Method - Tabular Learning and the Bellman Equation - Deep Q-Networks - Higher-Level RL Libraries - DQN Extensions - Ways to Speed up RL - Stocks Trading Using RL - Policy Gradients - an Alternative - Actor-Critic Methods - A2C and A3C - The TextWorld Environment - Web Navigation - Continuous Action Space - Trust Regions - PPO, TRPO, ACKTR, and SAC - Black-Box Optimization in RL - Advanced Exploration - RL with Human Feedback
520			_aStart your journey into reinforcement learning (RL) and reward yourself with the third edition of Deep Reinforcement Learning Hands-On. This book takes you from the basics of RL to more advanced concepts, using various applications, including game playing, discrete optimisation, stock trading, and web browser navigation. By walking you through landmark research papers in the field, this deep RL book will equip you with practical knowledge of RL and the theoretical foundation to understand and implement most modern RL papers. The book retains its approach of providing concise, easy-to-follow explanations from previous editions. You'll work through practical and diverse examples, from grid environments and games to stock trading and RL agents in web environments, to give you a well-rounded understanding of RL, its capabilities, and its use cases. You'll learn about key topics, such as deep Q-networks (DQNs), policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. If you want to learn about RL through a practical approach using OpenAI Gym and PyTorch, concise explanations, and the incremental development of topics, then Deep Reinforcement Learning Hands-On, Third Edition, is your ideal companion *Email sign-up and proof of purchase required What You Will Learn: - Stay on the cutting edge with new content on MuZero, RL with human feedback, and LLMs - Evaluate RL methods, including cross-entropy, DQN, actor-critic, TRPO, PPO, DDPG, and D4PG - Implement RL algorithms using PyTorch and modern RL libraries - Build and train deep Q-networks to solve complex tasks in Atari environments - Speed up RL models using algorithmic and engineering approaches - Leverage advanced techniques like proximal policy optimisation (PPO) for more stable training
650			_aDeep reinforcement learning _979334
650			_aMachine learning _979335
650			_aReinforcement learning _979336
650			_aArtificial intelligence _979337
942			_cBK
999			_c199995 _d199995