---

♟️ Monte Carlo Tree Search (MCTS) - A Comprehensive Guide

📝 Introduction

Monte Carlo Tree Search (MCTS) is a powerful search algorithm used in AI-driven decision-making, particularly in game-playing, robotics, and planning problems. This repository serves as a complete guide to understanding, implementing, and optimizing MCTS in Python with real-world examples.

📌 Understand the fundamentals of MCTS
📌 Explore the four key MCTS steps: Selection, Expansion, Simulation, Backpropagation
📌 Implement MCTS from scratch in Python
📌 Apply MCTS to board games like Chess, Go, and Tic-Tac-Toe
📌 Optimize MCTS using parallelization & deep learning

---

🚀 Features

• 📖 Detailed explanations of MCTS principles
• 🏗 Step-by-step implementation from scratch
• 🎲 Game-playing AI using MCTS (Tic-Tac-Toe, Chess, Go, etc.)
• ⚡ Performance optimizations (Parallel MCTS, pruning, heuristics)
• 📚 Jupyter notebooks with interactive explanations
• 🏆 Comparisons with Minimax & Alpha-Beta Pruning

---

📌 Prerequisites

Before running the code, make sure you have:

• Python 3.x → Download Here
• Libraries: NumPy, Matplotlib, SciPy
• Jupyter Notebook for interactive learning

---

🏆 Getting Started

1️⃣ Clone the Repository

git clone https://github.com/saadsalmanakram/MCTS.git
cd MCTS

2️⃣ Install Dependencies

pip install -r requirements.txt

3️⃣ Run an Example Notebook

Launch Jupyter Notebook and explore interactive notebooks:

jupyter notebook

---

🔍 Topics Covered

📖 Understanding MCTS

• The Four Phases: Selection, Expansion, Simulation, Backpropagation
• The UCT Formula (Upper Confidence Bound for Trees)
• Balancing exploration vs. exploitation

🏗 Implementing MCTS in Python

• Creating a Tree Node structure
• Running Monte Carlo simulations
• Selecting the best move using UCT

♟ Applying MCTS to Board Games

• Tic-Tac-Toe (Basic Example)
• Connect 4 (Medium Complexity)
• Chess & Go (Advanced AI)

🚀 Optimizing MCTS Performance

• Parallel MCTS for speedup
• Pruning strategies
• Using Neural Networks for move evaluation

---

🚀 Example Code

🎲 Basic MCTS Node Implementation

import math
import random

class Node:
    def __init__(self, state, parent=None):
        self.state = state
        self.parent = parent
        self.children = []
        self.visits = 0
        self.value = 0.0

    def select_best_child(self):
        return max(self.children, key=lambda child: child.value / (child.visits + 1e-6) + math.sqrt(2 * math.log(self.visits + 1) / (child.visits + 1)))

    def expand(self):
        new_state = self.state.get_next_state()  # Define this in your game logic
        child = Node(new_state, parent=self)
        self.children.append(child)
        return child

🎲 MCTS Algorithm Implementation

def mcts_search(root, iterations=1000):
    for _ in range(iterations):
        node = root
        while node.children:
            node = node.select_best_child()
        new_node = node.expand()
        reward = simulate(new_node.state)  # Run a Monte Carlo simulation
        backpropagate(new_node, reward)
    return max(root.children, key=lambda child: child.visits)

def backpropagate(node, reward):
    while node:
        node.visits += 1
        node.value += reward
        node = node.parent

🎲 MCTS for Tic-Tac-Toe

from tic_tac_toe import TicTacToe  # Assume TicTacToe class is defined

game = TicTacToe()
root = Node(game)
best_move = mcts_search(root, iterations=500)
print("Best Move:", best_move.state)

---

🏆 Real-World Applications

📌 Game AI → Chess, Go, Poker, Tic-Tac-Toe
📌 Robotics → Motion planning & decision-making
📌 Optimization Problems → Route planning, scheduling
📌 Autonomous Systems → AI-driven decision-making

---

🏆 Contributing

Contributions are welcome! 🚀

🔹 Fork the repository
🔹 Create a new branch (git checkout -b feature-name)
🔹 Commit changes (git commit -m "Added MCTS optimization example")
🔹 Push to your branch (git push origin feature-name)
🔹 Open a pull request

---

📜 License

This project is licensed under the MIT License – feel free to use, modify, and share the code.

---

📬 Contact

📧 Email: saadsalmanakram1@gmail.com
🌐 GitHub: SaadSalmanAkram
💼 LinkedIn: Saad Salman Akram

---

⚡ Master MCTS & Build Smarter AI Systems! ⚡

---