{"product_id":"139974","title":"reinforcement learning ","description":"\u003ccenter\u003e\u003cdiv style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/tmgdisk01.cafe24.com\/images\/vs\/4172\/sv\/3jXOZV81rkizzu1h0DIGQk11o97IhZ.png?v=1765077525\" style=\"max-width:100%;max-height:10px\"\u003e\u003c\/div\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\n\n\u003cdiv style=\"width:95%\"\u003e\n\n\u003cdiv style=\"text-align:center;font-size:30px;font-weight:bolder;line-height:1.6em\"\u003e reinforcement learning \u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"border-bottom:1px;border-bottom-style:dotted;border-color:;padding-bottom:20px\"\u003e\u003ccenter\u003e\u003ctable align=\"center\" width=\"100%\"\u003e\u003ctbody style=\"border:0px\"\u003e\n\n\u003ctr\u003e\u003ctd align=\"center\" style=\"line-height:1.2em;text-align:center;font-size:18px;color:black;font-weight:bold;padding-bottom:20px;\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\u003ctr\u003e\u003ctd style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/image.yes24.com\/goods\/149677992\/XL\" style=\"max-width:100%;height:auto\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\n\u003c\/tbody\u003e\u003c\/table\u003e\u003c\/center\u003e\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;{split_style6}padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e Description \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;word-break:break-all;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eBook Introduction\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\u003cdiv\u003e \u003cb\u003eUnderstand key concepts and algorithms\u003c\/b\u003e \u003cbr\u003eA textbook on reinforcement learning implemented through practice\u003cbr\u003e\u003cbr\u003e \"Reinforcement Learning\" is aimed at readers who want to systematically learn reinforcement learning from the basics to its applications.\u003cbr\u003e First, we introduce a new idea for solving the problem and explain the basic principles that support it.\u003cbr\u003e Next, we present a formula-based algorithm and organize the content so that students can implement the algorithm in a real-world environment and evaluate its learning performance by practicing programs using TensorFlow and Gymnasium.\u003cbr\u003e It also covers everything from traditional dynamic programming to the latest algorithms such as DQN, PPO, and SAC, and faithfully reflects advanced application areas such as autonomous driving, intelligent robots, and AGI, as well as the latest research trends.\u003cbr\u003e\u003cbr\u003e ※ This book was developed as a textbook for university lectures, so it does not provide answers to practice problems.\u003cbr\u003e\n\n\u003c\/div\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cul\u003e\u003cli\u003e You can preview some of the book's contents.\u003cbr\u003e \u003cspan\u003ePreview\u003c\/span\u003e\n\n\u003c\/li\u003e\u003c\/ul\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eindex\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e  \u003cdiv\u003eCHAPTER 01 Introduction\u003cbr\u003e 1 What is reinforcement learning?\u003cbr\u003e 2 Machine Learning = Supervised Learning + Unsupervised Learning + Reinforcement Learning\u003cbr\u003e 3 Success Stories and Applications\u003cbr\u003e 4 Brief History\u003cbr\u003e 5 Readings\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e CHAPTER 02 Establishing the Basics of Reinforcement Learning\u003cbr\u003e 1 Agents that interact with the environment\u003cbr\u003e 2 MDP Programming with Python\u003cbr\u003e 3 Comparison of expected gains between random and optimal policies\u003cbr\u003e 4 Policy and Value Functions\u003cbr\u003e 5 Understanding the Difficulty and Approaches of Reinforcement Learning\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e CHAPTER 03 Dynamic Programming\u003cbr\u003e 1 Principle\u003cbr\u003e 2 Bellman equation and policy iteration algorithm\u003cbr\u003e 3 Bellman Optimality Equation and Value Iteration Algorithm\u003cbr\u003e 4 Dynamic Programming of Stochastic Tasks\u003cbr\u003e 5 Characteristics and Limitations of Dynamic Programming\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e CHAPTER 04 Monte Carlo Method\u003cbr\u003e 1 episode generator\u003cbr\u003e 2 Balance of Exploration and Exploration\u003cbr\u003e 3 Policy Evaluation Using the Monte Carlo Method\u003cbr\u003e 4 Policy learning using the Monte Carlo method\u003cbr\u003e 5 Performance Improvement Techniques\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e CHAPTER 05 Time-Differential Learning\u003cbr\u003e 1 Principle\u003cbr\u003e 2 Policy Evaluation\u003cbr\u003e 3 Sarsa\u003cbr\u003e 4 Q-Learning\u003cbr\u003e Learning Blackjack with Q-Learning \u003cbr\u003e6 Training CartPole with Q-Learning\u003cbr\u003e 7 Performance Improvement Techniques\u003cbr\u003e 8 Expanding the perspective\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e CHAPTER 06 Approximation Methods Using Neural Networks\u003cbr\u003e 1 Neural Network Basics\u003cbr\u003e 2 Neural network implementation\u003cbr\u003e Q-learning using 3 neural networks\u003cbr\u003e 4 Neural Network-Based Q-Learning Implementation: CartPole Task\u003cbr\u003e 5 Neural Network-Based Q-Learning Implementation: Blackjack Task\u003cbr\u003e 6 Controversies and New Paths for Neural Networks\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e CHAPTER 07 Deep Learning Methods\u003cbr\u003e 1 A major shift towards deep learning\u003cbr\u003e 2 DQNs\u003cbr\u003e 3 Replay Memory\u003cbr\u003e 4 Deep Learning Basics\u003cbr\u003e 5 Atari gaming environment\u003cbr\u003e 6 Pong Atari game using DQN\u003cbr\u003e 7 Additional remarks\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e CHAPTER 08 Policy Gradient Method\u003cbr\u003e 1 Policy-centered learning\u003cbr\u003e 2 REINFORCE algorithm\u003cbr\u003e 3 REINFORCE Programming: Discrete Tasks\u003cbr\u003e Policy gradient for 4 consecutive tasks\u003cbr\u003e 5 REINFORCE Programming: Continuous Tasks\u003cbr\u003e 6 Speeding Up Python's Array Operations\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e CHAPTER 09 The Activist-Critic Method\u003cbr\u003e 1. Collaboration between activists and critics\u003cbr\u003e 2 Bias-Variance Tradeoff\u003cbr\u003e 3 Profit function\u003cbr\u003e 4 A2C and A3C\u003cbr\u003e 5 A2C Programming: Discrete Tasks \u003cbr\u003e6 A2C Programming: Continuous Tasks\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e CHAPTER 10 TRUST REGION METHOD\u003cbr\u003e 1. Improvement of the single-note policy\u003cbr\u003e 2 TRPO algorithm\u003cbr\u003e 3 PPO algorithm\u003cbr\u003e 4 Improving the efficiency of PPO\u003cbr\u003e 5 PPO Programming: Sequential Tasks\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e CHAPTER 11 Combining Policy Optimization and DQN\u003cbr\u003e 1 Motivation and Development\u003cbr\u003e 2 DDPG learning algorithm\u003cbr\u003e 3 Programming Practice: Learning Hopper Tasks Using DDPG\u003cbr\u003e 4 TD3 learning algorithm\u003cbr\u003e 5 Programming Practice: Learning the Hopper Task Using TD3\u003cbr\u003e 6 SAC learning algorithms\u003cbr\u003e 7 Programming Practice: Learning Hopper Tasks with SAC\u003cbr\u003e 8 Benchmarking Analysis\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e CHAPTER 12 Learning by Mimicry\u003cbr\u003e 1. Idea and Development\u003cbr\u003e 2. Duplicate actions\u003cbr\u003e 3. Inverse reinforcement learning\u003cbr\u003e 4. Adversarial mimicry learning\u003cbr\u003e 5. Observation Mimicry\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e CHAPTER 13 ADVANCED APPLICATIONS\u003cbr\u003e 1 Advanced Products Built with Reinforcement Learning: Opportunities and Challenges\u003cbr\u003e 2 video games\u003cbr\u003e 3 board games\u003cbr\u003e 4 Large-Scale Language Models\u003cbr\u003e 5 Autonomous driving\u003cbr\u003e 6 robots\u003cbr\u003e 7 Towards Artificial General Intelligence\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e References\u003cbr\u003e Search\u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eDetailed image\u003c\/b\u003e \u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cdiv\u003e\u003cimg src=\"https:\/\/image.yes24.com\/momo\/TopCate5459\/MidCate1\/545803459.jpg\" border=\"0\" alt=\"Detailed Image 1\"\u003e\u003c\/div\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e GOODS SPECIFICS\u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;font-size:14px;line-height:1.6em;\"\u003e\n\n \u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e- \u003cstrong\u003eDate of issue:\u003c\/strong\u003e July 24, 2025\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003ePage count, weight, size:\u003c\/strong\u003e 536 pages | 1,059g | 188*257*21mm\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eISBN13:\u003c\/strong\u003e 9791173400070 \u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cspan\u003e\u003c\/span\u003e\n\n\u003c\/center\u003e\n\n\n\u003c\/center\u003e","brand":"LIBRAIRIE COREENNE","offers":[{"title":"Default Title","offer_id":43893389688874,"sku":"139974","price":54.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0683\/2750\/5962\/files\/9471fa89dff7670203d501dfb7dceee2.jpg?v=1765399807","url":"https:\/\/librairie.coreenne.fr\/en\/products\/139974","provider":"LIBRAIRIE COREENNE","version":"1.0","type":"link"}