Monte Carlo Tree Search In Python

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Monte Carlo Tree Search In Python - AD-TEAM - 02-12-2025

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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

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