🧊 AIM – Autonomous Rubik’s Cube Solver & RL Intelligence System

"Intelligence is the ability to adapt to change."

Developed by Nik | Final Year B.Tech (ECE), NIT Agartala

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📖 Table of Contents

• Overview
• Key Features
• System Architecture
• Installation & Setup
• User Manual
• Algorithmic Core
• Analytical Engine
• Reinforcement Learning Agent
• Performance Metrics
• Project Structure
• Future Roadmap
• Contact & License

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🔍 Overview

AIM (Autonomous Intelligent Model) is a dual-engine Rubik’s Cube intelligence system that unifies classical algorithmic solving with modern reinforcement learning research.

It is not just a cube solver— it is a simulation lab, built for experimentation, visualization, optimization, and AI behavior analysis.

AIM provides:

• ⚡ A high-performance solver capable of instantly solving any NxN cube (2×2 → 30×30)
• 🤖 A Q-Learning agent that learns how to solve a 2×2 cube from scratch
• 🎨 A Cyber-Tech Streamlit interface for real-time logs, visual cube states, charts & training analytics

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✨ Key Features

🧠 Analytical Engine (Expert System)

• Universal cube size support: 2×2 to 30×30

• Reverse-Scramble with Move Optimization

• <0.01s solution latency

• Intelligent move cleanup:

  • R R' → removed
  • U U → U2
  • F F2 → F'

• Real-time visual cube net rendering

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🤖 Reinforcement Learning Agent (Student AI)

• Tabular Q-Learning implementation for 2×2

• Adjustable hyperparameters (α, γ, ε)

• Training visualization with:

  • Reward convergence plots
  • Epsilon decay analysis

• Supports user-defined scramble depth & batch episodes

• Model testing after training

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💻 Cyber-Tech Interface

• Dark futuristic theme
• Scrollable system logs
• Clean sidebar controls
• Professional analytic visualizations

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⚙️ System Architecture

1. RubiksCubeSolver Class

• N×N matrix representation
• Uses numpy.rot90() for efficient face rotations
• Maintains full move history
• Reverse-Scramble + Optimizer for instant solutions

2. RubiksCubeAI Class

• State-Hash → Q-Values mapping
• Bellman Update function
• Training loop handling: Action → Environment Step → Reward → Q-Update
• Exploration vs. Exploitation balancing

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🛠 Installation & Setup

Prerequisites

• Python 3.8+
• pip package manager

Step 1 — Clone the Repository

git clone https://github.com/your-username/aim-rubiks-solver.git
cd aim-rubiks-solver

Step 2 — Install Dependencies

pip install streamlit numpy pandas

Step 3 — Launch the Application

streamlit run app.py

The app starts at: http://localhost:8501

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🕹 User Manual

Tab 1 — Analytical Solver (Expert Mode)

• Choose Cube Order (N)

• Click Scramble Cube

• Click Solve Cube

• Observe:

  • Move Optimization
  • Execution Time
  • Visual Cube State

Tab 2 — RL Agent Diagnostics (Research Mode)

• Set batch size & scramble depth
• Start training
• Watch reward convergence graph rise
• Test the agent’s learned policy

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🧮 Algorithmic Core

1. Analytical Engine — Deterministic Mathematics

For a scramble:

R U F

The inverse solution is:

F' U' R'

Then an optimizer refines:

• R R' → removed
• R R → R2
• R R2 → R'

This produces minimal, elegant move sequences.

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2. Reinforcement Learning Agent — Q-Learning

• State (S): Flattened color hash of cube

• Actions (A): 12 face turns

• Reward (R):

  • +100 for solving
  • -0.1 per move

Q-Update Rule:

Q(S,A) ← Q(S,A) + α [ R + γ max(Q(S'),A') − Q(S,A) ]

The agent gradually discovers efficient solve paths.

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📊 Performance Metrics

Engine Type	Cube Size	Success Rate	Avg. Time
Analytical	3×3	100%	~0.001s
Analytical	20×20	100%	~0.005s
RL Agent	2×2	~85%	Varies

RL performance depends on training duration and scramble depth.

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📂 Project Structure

AIM-Rubiks-Solver/
│── app.py               # Main Application
│── README.md            # Documentation
│── requirements.txt     # Dependencies
└── assets/              # Images / diagrams

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🚀 Future Roadmap

• 3D Cube Visualization (Three.js / PyDeck)
• Deep Q-Network (DQN) for 3×3 learning capability
• Kociemba’s Two-Phase Algorithm integration
• Exportable solution logs
• Performance benchmarking suite

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🤝 Contact & License

Developer: Nik Role: Final Year B.Tech (ECE) Institution: NIT Agartala

License: MIT License

Made with ❤️ and Python.