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Got it! I'll find the best resources to get you up to speed on Monte Carlo Tree Search (MCTS) without reinventing the wheel. This will include:

The most cited and accessible foundational papers.
High-quality GitHub repositories with clear implementations.
Tutorials or blog posts that explain MCTS concepts and provide code examples.

I'll get back to you shortly with the best materials!

Best Resources for Learning Monte Carlo Tree Search (MCTS)

Learning MCTS efficiently involves understanding the core algorithm and having practical examples to follow. Below are top resources grouped by foundational readings, tutorials, and code implementations, so you can quickly grasp MCTS concepts and apply them without reinventing the wheel.

sudo apt-get update
sudo apt-get install -y build-essential git cuda-libraries-dev-12-8 libopencv-dev gpg wget
wget -O - https://apt.kitware.com/keys/kitware-archive-latest.asc 2>/dev/null | gpg --dearmor - | sudo tee /usr/share/keyrings/kitware-archive-keyring.gpg >/dev/null

echo 'deb [signed-by=/usr/share/keyrings/kitware-archive-keyring.gpg] https://apt.kitware.com/ubuntu/ <distro> main' | sudo tee /etc/apt/sources.list.d/kitware.list >/dev/null

sudo apt-get update
sudo apt-get install gcc-12 g++-12
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-12 12

	import torch, torch.nn.functional as F
	from transformers import AutoModelForCausalLM, AutoTokenizer, AutoProcessor
	from PIL import Image

	REVISION = "2025-04-14" # lock to a known good tag
	MODEL_ID = "vikhyatk/moondream2"
	device = "cuda" # or "cpu" / bitsandbytes / GGUF, etc.

	# 1️⃣ load model + text tokenizer
	model = AutoModelForCausalLM.from_pretrained(

	import torch, torch.nn.functional as F
	from transformers import AutoProcessor, LlavaForConditionalGeneration
	from PIL import Image

	MODEL_ID = "llava-hf/llava-1.5-7b-hf"
	device = "cuda"

	model = LlavaForConditionalGeneration.from_pretrained(
	MODEL_ID, torch_dtype=torch.float16, device_map="auto")
	processor = AutoProcessor.from_pretrained(MODEL_ID)

	# -- coding: utf-8 --
	"""
	CyCNN implementation for rotation invariant image classification.
	Based on "CyCNN: A Rotation Invariant CNN using Polar Mapping and Cylindrical Convolution Layers"
	"""

	# Core Libraries
	import os
	import random
	import time



	<!DOCTYPE html>
	<html lang="en">
	<head>
	<meta charset="UTF-8">
	<meta name="viewport" content="width=device-width, initial-scale=1.0">
	<title>Chips Filter Demo</title>
	<style>
	body {

	def improved_auto_color(image: Image.Image) -> Image.Image:
	"""
	Improved version of Photoshop-like Auto Color.
	It works by:
	1. Converting to float and normalizing.
	2. Computing per-channel percentiles (e.g., 0.5% and 99.5%).
	3. Stretching values between those percentiles to [0, 255].
	4. This avoids over-amplifying noise or outliers.
	"""
	img_array = np.array(image).astype(np.float32)

	#!/usr/bin/env python3
	import sys
	import os
	from google import genai
	from google.genai import types
	from PIL import Image
	import readline # For command history

	def print_usage():
	"""Print usage instructions."""

	import json
	import matplotlib.pyplot as plt
	import numpy as np
	import pandas as pd
	from collections import Counter
	from datetime import datetime, timedelta
	import seaborn as sns
	import matplotlib.dates as mdates
	from matplotlib.gridspec import GridSpec
	import re

	import random

	# Generate random byte data
	N = 8 # Number of chunks
	byte_data = bytes(random.getrandbits(8) for _ in range(1024)) # Generate 1024 random bytes

	# Split into N equal chunks using plain Python
	chunk_size = len(byte_data) // N
	split_data = [byte_data[i * chunk_size : (i + 1) * chunk_size] for i in range(N)]