{"product_id":"138665","title":"Learning reinforcement learning through code like a developer ","description":"\u003ccenter\u003e\u003cdiv style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/tmgdisk01.cafe24.com\/images\/vs\/4172\/sv\/3jXPCfLD4CyqSv1ICFel0HlFD6f3lv.png?v=1765066268\" 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 through code, like a developer \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\/151347658\/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\u003eWriting Reinforcement Learning for Developers!\u003cbr\u003e Even if you were daunted by math, you can now start reinforcement learning with code.\u003cbr\u003e A practical introductory guide with real-world examples, from Stable Baselines3 to Optuna!\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003eReinforcement learning is overwhelming, and you don't know where to start? The math is difficult, the practical aspects are complex, and the concepts are abstract... That's why this book starts differently.\u003cbr\u003e\u003cbr\u003e \"Learn Reinforcement Learning with Code, Like a Developer\" is a practical introductory book designed to reduce the vague distance and mathematical burden associated with reinforcement learning and enable learning AI in a developer-friendly manner.\u003cbr\u003e It minimizes complex mathematical theories and formulas, organizes concepts so that they can be understood intuitively, and explains how the concepts are connected to actual code through various examples and visual aids.\u003cbr\u003e A balanced mix of theory and practice, accessible explanations, and projects based on real-world problems help students naturally embody reinforcement learning from concept to implementation.\u003cbr\u003e\u003cbr\u003e \u003cbr\u003eStarting with hands-on training in the OpenAI Gym environment, you can directly implement representative algorithms such as DQN, A2C, and PPO, and experience advanced implementations using the Stable-Baselines3 framework and the Optuna automatic tuning tool.\u003cbr\u003e Through examples close to real-world tasks, such as asset allocation and rotational work arrangements, you can connect to real-world situations without wondering, \"Where can I use this?\"\u003cbr\u003e This book is a friendly guide for beginner developers new to reinforcement learning, and a hands-on guide that develops critical thinking and application skills through repetitive code practice.\u003cbr\u003e If you're a developer who wants to design your own strategy, start with this book now.\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 \",\"\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eindex\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e To begin with\u003cbr\u003e\u003cbr\u003e Chapter 1: Basic Concepts of Reinforcement Learning\u003cbr\u003e _1.1 What is reinforcement learning?\u003cbr\u003e _1.2 Probability and Stochastic Processes\u003cbr\u003e __1.2.1 Probability\u003cbr\u003e __1.2.2 Conditional probability\u003cbr\u003e __1.2.3 Stochastic Processes\u003cbr\u003e _1.3.\u003cbr\u003e Markov chain\u003cbr\u003e __1.3.1 Markov property \u003cbr\u003e__1.3.2 Markov property and Brownian motion\u003cbr\u003e __1.3.3 Markov chain\u003cbr\u003e _1.4 Markov Reward Process\u003cbr\u003e __1.4.1 Composition of Markov Rewards\u003cbr\u003e __1.4.2 What is the expected value of probability?\u003cbr\u003e __1.4.3 Return value\u003cbr\u003e __1.4.4 State Value Function\u003cbr\u003e\u003cbr\u003e Chapter 2: Basic Reinforcement Learning Algorithms\u003cbr\u003e _2.1 What is a Markov decision process?\u003cbr\u003e _2.2 MDP Components\u003cbr\u003e __2.2.1 State transition matrix and reward function in MDP\u003cbr\u003e __2.2.2 Policy in MDP\u003cbr\u003e __2.2.3 Comparison Case of MRP and MDP\u003cbr\u003e __2.2.4 State Transition Matrix and Reward Function Considering Policy\u003cbr\u003e _2.3 MDP State Value Function\u003cbr\u003e __2.3.1 What is the MDP state value function?\u003cbr\u003e __2.3.2 MDP State Value Function Example\u003cbr\u003e _2.4 MDP Action Value Function\u003cbr\u003e __2.4.1 What is an MDP action value function?\u003cbr\u003e __2.4.2 Relationship between MDP action value function and state value function\u003cbr\u003e __2.4.3 MDP Action Value Function Example\u003cbr\u003e _2.5 MDP optimal value function\u003cbr\u003e __2.5.1 What is the MDP optimal value function?\u003cbr\u003e __2.5.2 MDP Optimal Value Function Example\u003cbr\u003e _2.6 Various terms used in reinforcement learning\u003cbr\u003e __2.6.1 Policy Evaluation and Policy Control\u003cbr\u003e __2.6.2 Model-based and model-free\u003cbr\u003e \u003cbr\u003eChapter 3: Dynamic Programming and Monte Carlo Methods\u003cbr\u003e _3.1.\u003cbr\u003e dynamic programming\u003cbr\u003e __3.1.1.\u003cbr\u003e What is dynamic programming?\u003cbr\u003e __3.1.2.\u003cbr\u003e Grid World\u003cbr\u003e __3.1.3 Dynamic Programming Example\u003cbr\u003e _3.2 Monte Carlo method\u003cbr\u003e __3.2.1 What is the Monte Carlo method?\u003cbr\u003e __3.2.2 Monte Carlo method using incremental averaging\u003cbr\u003e __3.2.3 Monte Carlo method example\u003cbr\u003e\u003cbr\u003e Chapter 4: Time-Differential Learning, Salsa, and Q-Learning\u003cbr\u003e _4.1 Time-lapse learning\u003cbr\u003e __4.1.1 What is temporal difference learning?\u003cbr\u003e __4.1.2 Action value function rather than state value function\u003cbr\u003e __4.1.3 Time-Differential Learning Example\u003cbr\u003e _4.2 Salsa\u003cbr\u003e __4.2.1 SARSA Concept\u003cbr\u003e __4.2.2 Salsa Example\u003cbr\u003e _4.3 Q Running\u003cbr\u003e __4.3.1 On-policy and off-policy\u003cbr\u003e __4.3.2 Importance Sampling\u003cbr\u003e __4.3.3 Q Running\u003cbr\u003e __4.3.4 Q-Learning Example\u003cbr\u003e __4.3.4 Comparison of Salsa and Q-Learning Examples\u003cbr\u003e\u003cbr\u003e Chapter 5: Artificial Intelligence Concepts\u003cbr\u003e _5.1 Machine Learning\u003cbr\u003e _5.2 Linear Regression Analysis\u003cbr\u003e _5.3 Classification Analysis\u003cbr\u003e _5.4 Deep Learning\u003cbr\u003e _5.5 Learning the Basics of Artificial Intelligence with Programs\u003cbr\u003e __5.5.1 What is TensorFlow?\u003cbr\u003e __5.5.2 Basic Artificial Neural Network Example\u003cbr\u003e\u003cbr\u003e Chapter 6 Function Approximation\u003cbr\u003e _6.1 Differentiation\u003cbr\u003e _6.2 Partial differentiation \u003cbr\u003e_6.3 Scalars and Vectors\u003cbr\u003e _6.4 Gradient\u003cbr\u003e _6.5 Gradient descent\u003cbr\u003e _6.6 Stochastic Gradient Descent\u003cbr\u003e _6.7 Notation for Partial Differentiation and Gradient Descent in Reinforcement Learning\u003cbr\u003e _6.8 Function Approximation\u003cbr\u003e\u003cbr\u003e Chapter 7: Value-Based Reinforcement Learning and the DQN Algorithm\u003cbr\u003e _7.1 DQN Algorithm\u003cbr\u003e _7.2 Cartpole\u003cbr\u003e _7.3 The Problem of Exploration and Greed\u003cbr\u003e _7.4 Basic Structure of the DQN Algorithm\u003cbr\u003e _7.5 Full DQN Algorithm Code Review\u003cbr\u003e _7.6 Detailed Structure of the DQN Algorithm\u003cbr\u003e _7.7 Analysis of DQN Algorithm Learning Results\u003cbr\u003e\u003cbr\u003e Chapter 8 Policy-Based Reinforcement Learning REINFORCE Algorithm\u003cbr\u003e _8.1 Revisiting Artificial Neural Networks\u003cbr\u003e _8.2 Policy Gradient\u003cbr\u003e _8.3 How the REINFORCE Algorithm Works\u003cbr\u003e _8.4 Basic Structure of the REINFORCE Algorithm\u003cbr\u003e _8.5 REINFORCE Algorithm Full Code Review\u003cbr\u003e _8.6 Detailed Structure of the REINFORCE Algorithm\u003cbr\u003e _8.7 Analysis of REINFORCE Algorithm Learning Results\u003cbr\u003e\u003cbr\u003e Chapter 9 Policy-Based A2C Algorithm\u003cbr\u003e _9.1 Actor Critic Algorithm\u003cbr\u003e __9.1.1 What is the Actor Critic Algorithm?\u003cbr\u003e __9.1.2 Actor Critic Algorithm Structure and Operation\u003cbr\u003e _9.2 Advantage Actor Critic \u003cbr\u003e_9.3 Basic Structure of the A2C Algorithm\u003cbr\u003e _9.4 A2C Algorithm Full Code Review\u003cbr\u003e _9.5 A detailed look at the A2C algorithm structure\u003cbr\u003e _9.6 Analysis of A2C Algorithm Learning Results\u003cbr\u003e Chapter 10 Policy-Based PPO Algorithm\u003cbr\u003e\u003cbr\u003e _10.1 Importance Sampling\u003cbr\u003e _10.2 On-policy policy gradient\u003cbr\u003e _10.3 Clipping Technique\u003cbr\u003e _10.4 GAE\u003cbr\u003e _10.5 Basic Structure of the PPO Algorithm\u003cbr\u003e _10.6 Full Code Review of the PPO Algorithm\u003cbr\u003e _10.7 Detailed Structure of the PPO Algorithm\u003cbr\u003e _10.8 PPO Algorithm Algorithm Learning Results Analysis\u003cbr\u003e\u003cbr\u003e Chapter 11: Tuning Artificial Neural Networks\u003cbr\u003e _11.1 Overview of Artificial Neural Network Tuning\u003cbr\u003e _11.2 Input data preprocessing\u003cbr\u003e __11.2.1 Standardization\u003cbr\u003e __11.2.2 Normalization\u003cbr\u003e _11.3 Choosing a Cost Function\u003cbr\u003e _11.4 Activation Algorithm\u003cbr\u003e _11.5 Weight Initialization\u003cbr\u003e _11.6 Optimization Algorithm\u003cbr\u003e _11.7 Discussion on the number of nodes and hidden layers\u003cbr\u003e _11.8 Other Model Training Stabilization and Performance Improvement Techniques\u003cbr\u003e __11.8.1 Gradient Clipping\u003cbr\u003e __11.8.2 Early Termination\u003cbr\u003e _11.9 PPO Algorithm Artificial Neural Network Tuning\u003cbr\u003e _11.10 Applying PPO algorithm tuning code\u003cbr\u003e _11.11 Analysis of PPO Algorithm Tuning Results\u003cbr\u003e \u003cbr\u003eChapter 12 Bayesian Optimization Techniques\u003cbr\u003e _12.1 Frequentist and Bayesian Probability\u003cbr\u003e _12.2 Calculating Bayesian Probability\u003cbr\u003e _12.3 Bayesian Optimization Package Optuna\u003cbr\u003e __12.3.1 Optuna Example\u003cbr\u003e _12.4 optuna optimization full code\u003cbr\u003e _12.5 Analysis of Bayesian Optimization Results\u003cbr\u003e\u003cbr\u003e Chapter 13 Stable Baselines 3\u003cbr\u003e _13.1 What is Stable-Baselines3?\u003cbr\u003e __13.1.1 Introduction to Open-Source Reinforcement Learning Frameworks\u003cbr\u003e __13.1.2 Relationship with Baselines, Evolution from SB2 to SB3\u003cbr\u003e __13.1.3 Why SB3? Direct Implementation vs.\u003cbr\u003e Using the library\u003cbr\u003e _13.2 Core Components of SB3\u003cbr\u003e __13.2.1 Policy: The Agent's Brain\u003cbr\u003e __13.2.2 Model: The Center of Learning\u003cbr\u003e __13.2.3 Env: Link to the environment\u003cbr\u003e __13.2.4 Callback: Control and Monitor During Learning\u003cbr\u003e __13.2.5 VecEnv: Speeding Up with Parallel Environments\u003cbr\u003e _13.3 Key Components of SB3 as Seen Through Examples\u003cbr\u003e __13.3.1 Understanding SB3 Core Components\u003cbr\u003e __13.3.2 SB3 PPO class hyperparameters\u003cbr\u003e __13.3.3 SB3 PPO class tuning applied\u003cbr\u003e __13.3.4 Running Example Code and Evaluation Metrics\u003cbr\u003e __13.3.5 Key Indicators for Interpreting Learning Outcomes \u003cbr\u003e__13.3.6 TensorBoard\u003cbr\u003e\u003cbr\u003e Chapter 14: AI Asset Allocation Strategies\u003cbr\u003e _14.1 What is an asset allocation strategy?\u003cbr\u003e _14.2 Ray Dalio's All Weather Portfolio\u003cbr\u003e __14.2.1 What is an All Weather Portfolio?\u003cbr\u003e __14.2.2 Roles by Asset Class\u003cbr\u003e __14.2.3 Ray Dalio's Asset Allocation\u003cbr\u003e __14.2.4 Pros and Cons of the All-Weather Portfolio\u003cbr\u003e __14.2.5 All-Weather Portfolio Investment Performance Evaluation\u003cbr\u003e __14.2.6 All-Weather Portfolio and Reinforcement Learning\u003cbr\u003e _14.3 All-Weather Portfolio Code Implementation Strategy\u003cbr\u003e _14.4 All Weather Portfolio Full Code\u003cbr\u003e _14.5 Detailed Structure of the All-Weather Portfolio\u003cbr\u003e\u003cbr\u003e Chapter 15: Tuning AI Asset Allocation Strategies\u003cbr\u003e _15.1 First Tuning: Applying Empirical Tuning Techniques\u003cbr\u003e _15.2 Second Tuning: Utilizing the Optuna Package\u003cbr\u003e _15.3 Comparison with the Buy and Hold Strategy\u003cbr\u003e _15.4 Additional tuning elements\u003cbr\u003e\u003cbr\u003e [Appendix A] Preparing the Practice Environment\u003cbr\u003e _A.1 Preparing for Learning\u003cbr\u003e __A.1.1 Installing Python\u003cbr\u003e __A.1.2 Installing required packages\u003cbr\u003e _A.2 Running Jupyter Notebook\u003cbr\u003e _A.3 Check the program version\u003cbr\u003e\u003cbr\u003e [Appendix B] ChatGPT Evolved into RLHF: The Intersection of Generative AI and Reinforcement Learning \u003cbr\u003e_A.1 How were generative AI and Chat GPT created?\u003cbr\u003e __A.1.1 The concept of generative AI\u003cbr\u003e __A.1.2 How was ChatGPT trained?\u003cbr\u003e __A.1.3 How does GPT understand and speak context?\u003cbr\u003e __A.1.4 Summary\u003cbr\u003e _A.2 What is RLHF?\u003cbr\u003e __A.2.1 What is human feedback?\u003cbr\u003e __A.2.2 Why does ChatGPT need a reward model to learn 'good answers'?\u003cbr\u003e __A.2.3 Overall learning process of RLHF\u003cbr\u003e _A.3 Connecting PPO and the Policy-Based Algorithms We Learned\u003cbr\u003e __A.3.1 PPO we learned and PPO actually used\u003cbr\u003e __A.3.2 How did ChatGPT become a representative example of policy-based reinforcement learning?\u003cbr\u003e __A.3.3 The Position of PPO in the Flow of Practical Reinforcement Learning\u003cbr\u003e _A.4 Understanding RLHF through examples\u003cbr\u003e __A.4.1 Full Code Review\u003cbr\u003e __A.4.2 Understanding the Code\u003cbr\u003e _A.5 How will reinforcement learning evolve generative AI?\u003cbr\u003e __A.5.1 The Sorting Problem and the Role of Reinforcement Learning\u003cbr\u003e __A.5.2 Future Evolution Directions of Language Models\u003cbr\u003e __A.5.3 A Philosophical Shift in Reinforcement Learning Toward 'Human-Centered AI'\u003cbr\u003e \u003cbr\u003eSearch\u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\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\/TopCate5560\/MidCate003\/555921034.jpg\" border=\"0\" alt=\"Detailed Image 1\"\u003e\u003c\/div\u003e\u003c\/div\u003e \",\"\u003cdiv\u003e\u003ch5\u003e \u003cb\u003ePublisher's Review\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e This book was written for countless programmers who hesitated before the mathematical theory and complex code of reinforcement learning.\u003cbr\u003e Reinforcement learning, which has been known since the emergence of AlphaGo as an \"artificial intelligence that is good at games,\" is actually an autonomous and flexible learning method that discovers optimal strategies on its own through continuous interaction with the environment.\u003cbr\u003e These characteristics make them ideally suited to solving real-world problems characterized by unpredictability and complexity, and are particularly well-established as a viable weapon for profit generation in financial markets, including automated trading systems, high-frequency trading strategies, and risk management models.\u003cbr\u003e\u003cbr\u003e This book is not simply a theoretical book; it is a practical guide that helps readers leverage reinforcement learning to achieve meaningful results in real-world markets. \u003cbr\u003eThe following are the main features and structure of this book.\u003cbr\u003e\u003cbr\u003e \u003cb\u003eTarget audience for this book\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e Developers who want to study reinforcement learning but feel the barrier of mathematics or theoretical explanations\u003cbr\u003e · A programmer who wants to go beyond simple task automation and create an intelligent system that can make decisions and adapt to the situation.\u003cbr\u003e · Individual investors who are interested in asset markets such as stocks, cryptocurrencies, and raw materials and want to design their own profit-generating strategies.\u003cbr\u003e · Startup founders or planners planning AI investment apps, robo-advisors, and financial SaaS products utilizing reinforcement learning\u003cbr\u003e · Practitioners who want to automate repetitive transactions or workflows and empower them with learning and adaptability.\u003cbr\u003e\u003cbr\u003e \u003cb\u003eStructure of this book\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e This book is broadly divided into seven main parts and an appendix.\u003cbr\u003e Each part follows the flow of concept → implementation → practice → application, and the difficulty level increases step by step. \u003cbr\u003e· Basic concepts of reinforcement learning - Intuitive explanation of probability, Markov chain, MDP, value function, and Bellman equation.\u003cbr\u003e · Basic Algorithms - Learn core techniques such as dynamic programming, Monte Carlo, TD learning, SARSA, and Q learning, along with code.\u003cbr\u003e · Artificial Intelligence Concepts \u0026amp; Function Approximation - Covers the principles of neural networks, gradient descent, activation functions, and how to use deep learning frameworks.\u003cbr\u003e Value-based Reinforcement Learning (DQN) - Implement DQN with the CartPole example and practice exploration-exploitation balance and replay memory.\u003cbr\u003e · Tuning and Optimization - Practice activation functions, weight initialization, optimizers, data preprocessing, loss functions, gradient clipping, and Optuna-based Bayesian optimization.\u003cbr\u003e · Financial Market Project - Create an asset allocation environment using yfinance data and perform learning, tuning, and performance analysis with PPO.\u003cbr\u003e · Appendix - Guide to building a practice environment, in-depth explanation of RLHF\u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e \"]\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 August 25, 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 542 pages | 182*232*35mm\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 9788965404200\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eISBN10:\u003c\/strong\u003e 8965404207 \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":43893270511658,"sku":"138665","price":41.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0683\/2750\/5962\/files\/5f4e827fc2c473793e3f98b9124bebb4.jpg?v=1765394980","url":"https:\/\/librairie.coreenne.fr\/en\/products\/138665","provider":"LIBRAIRIE COREENNE","version":"1.0","type":"link"}