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    Monte Carlo Tree Search In Python

    Posted By: ELK1nG
    Monte Carlo Tree Search In Python

    Monte Carlo Tree Search In Python
    Published 2/2025
    MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
    Language: English | Size: 1.48 GB | Duration: 7h 6m

    Solve Business Problems with MCTS: Hands-on, and Spelled out Approach

    What you'll learn

    Fundamental Theory and Hands on Practice on Monte Carlo Simulation

    Tree Data Structure

    Tree Search Algorithms

    Theory of Monte Carlo Tree Search

    Hands-on Coding on Applying Monte Carlo Tree Search for Solving Job Shop Scheduling Problem

    Learn How to Apply Monte Carlo Tree Search to Other Practical Real-world Problems

    Requirements

    Basic understanding of Python programming language would be a great help.

    No experience of Reinforcement Learning or any other optimization algorithms is needed. You will need all the required theory in this course.

    Description

    Unlock the power of Monte Carlo Tree Search (MCTS) and learn how to apply this cutting-edge algorithm to real-world business challenges! In this hands-on course, we’ll take you from the foundational theory of Monte Carlo simulations to advanced MCTS implementations, all in Python.What makes MCTS truly practical is its versatility. Whether you're optimizing supply chain logistics, scheduling complex tasks, enhancing game AI, or making strategic business decisions under uncertainty, MCTS shines where traditional algorithms struggle. Its ability to balance exploration and exploitation makes it perfect for solving problems with large, dynamic, and unpredictable environments—just like in real-world business scenarios.You’ll start with the basics—understanding Monte Carlo simulations and Python coding strategies. Then, we’ll dive deep into tree search algorithms like BFS and DFS, setting the stage for mastering MCTS. Through step-by-step coding sessions, you'll implement key MCTS components: rollout, selection, expansion, and backpropagation.But we don’t stop at theory. You’ll solve practical business problems, including job shop scheduling, using MCTS with real-world data. We’ll guide you through designing code structures, optimizing performance, and analyzing results effectively.By the end of this course, you'll not only understand how MCTS works but also how to apply it confidently to complex decision-making problems.

    Overview

    Section 1: Introduction

    Lecture 1 Introduction

    Section 2: Monte Carlo Simulation

    Lecture 2 Set up coding IDE

    Lecture 3 Monte Carlo Simulation: Theory

    Lecture 4 Hands-on Practice with Monte Carlo Simulation in Python- Part 1

    Lecture 5 Hands-on Practice with Monte Carlo Simulation in Python - Part 2

    Lecture 6 Results for Monte Carlo Simulation in Python

    Section 3: Tree Search Algorithms

    Lecture 7 Introduction to Tree Data Structures

    Lecture 8 Breadth First Search, and Depth First Search Algorithms: Theory

    Lecture 9 Recursion Concept

    Lecture 10 Hands-on Code: Implement State Class

    Lecture 11 Hands-on Code: Implement State Transition

    Lecture 12 Hands-on Code: Implement BFS Algorithm

    Lecture 13 Hands-on Code: Implement DFS Algorithm

    Lecture 14 Hands-on Code: Implement Main Loop

    Lecture 15 Hands-on Code: Obtain Results

    Section 4: Monte Carlo Tree Search Algorithm

    Lecture 16 MCTS Theory: Fundamentals

    Lecture 17 MCTS Theory: Algorithm and Steps

    Lecture 18 MCTS Theory: Numerical Example

    Lecture 19 Hands-on Code: Overall Schema for MCTS Coding

    Lecture 20 Hands-on Code: Implement Rollout Module

    Lecture 21 Hands-on Code: Implement Select and Expand Modules

    Lecture 22 Hands-on Code: Implement Simulation and Backpropagation Modules

    Lecture 23 Hands-on Code: Implement UCB and Greedy Action Selection

    Lecture 24 Hands-on Code: Implement Abstract Class for Node

    Lecture 25 Hands-on Code: Define a Class for Job Shop Scheduling Problem

    Lecture 26 Hands-on Code: Implement Find Children Module

    Lecture 27 Hands-on Code: Implement Find Random Child Module

    Lecture 28 Hands-on Code: Implement Terminal and Reward Modules

    Lecture 29 Hands-on Code: Obtain the First Results and Expriments

    Lecture 30 Hands-on Code: Obtain Schedule

    Lecture 31 Hands-on Code: Implement Constraints Validation Function

    Lecture 32 Hands-on Code: Implement Gantt Chart Function

    Lecture 33 Conclusion

    Applied Data scientists who are dealing with solving real-world problems.,Researchers who want to apply MCTS or combine their approach with MCTS.,Game developers who want to learn one of the most required algorithms for game development.,Operations research scientists who want to add new, yet powerful weapon to their optimization arsenal.,Planning and Scheduling Specialists who want to apply simple yet efficient algorithm to solve their daily complex planning tasks