https://github.com/saadsalmanakram/mcts
Monte Carlo Tree Search
https://github.com/saadsalmanakram/mcts
mcts mcts-agents mcts-algorithm mcts-implementations mcts-solver
Last synced: 6 months ago
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Monte Carlo Tree Search
- Host: GitHub
- URL: https://github.com/saadsalmanakram/mcts
- Owner: saadsalmanakram
- Created: 2024-09-13T12:44:53.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-04-05T09:53:11.000Z (6 months ago)
- Last Synced: 2025-04-05T10:30:04.601Z (6 months ago)
- Topics: mcts, mcts-agents, mcts-algorithm, mcts-implementations, mcts-solver
- Homepage:
- Size: 56.9 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
---
# ♟️ 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](https://www.python.org/downloads/)
- Libraries: NumPy, Matplotlib, SciPy
- Jupyter Notebook for interactive learning
---
## 🏆 Getting Started
### 1️⃣ Clone the Repository
```bash
git clone https://github.com/saadsalmanakram/MCTS.git
cd MCTS
```
### 2️⃣ Install Dependencies
```bash
pip install -r requirements.txt
```
### 3️⃣ Run an Example Notebook
Launch Jupyter Notebook and explore interactive notebooks:
```bash
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**
```python
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**
```python
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**
```python
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](https://github.com/saadsalmanakram)
💼 **LinkedIn:** [Saad Salman Akram](https://www.linkedin.com/in/saadsalmanakram/)
---
⚡ **Master MCTS & Build Smarter AI Systems!** ⚡
---