🐍 Snake AI using Deep Q-Learning

This project implements an AI agent to play the classic Snake game using Deep Q-Learning (DQN), a reinforcement learning algorithm. The game environment is built using PyGame, and the AI is trained using PyTorch.

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📌 Problem Statement

Can an AI agent learn to play and master the classic Snake game using reinforcement learning techniques?

This project explores how Deep Q-Learning can be used to teach an AI agent to:

• Navigate the game grid
• Eat food to increase score
• Avoid collisions with walls and its own body
• Maximize performance through training

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🎮 Game Overview

• Snake moves in a grid environment.
• Eats food to grow and increase score.
• Dies on collision with wall or itself.
• The agent must learn efficient and safe navigation.

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🧠 Deep Q-Learning (DQN)

• DQN is a value-based reinforcement learning algorithm.
• The agent uses a neural network to approximate Q-values.
• Key components:
  • State: 11-dimensional binary vector representing current game state
  • Action: [straight, right, left]
  • Reward:
    • +10 for eating food
    • -10 for dying
    • -0 per step (to encourage faster solutions)
  • Experience Replay: Stores past experiences and trains on batches
  • Target Network: Helps stabilize learning

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🎲 Epsilon-Greedy Strategy

• Balances exploration vs exploitation.
• Starts with high ε (exploration), gradually decays over time.
• Random action with probability ε.
• Best-known action with probability 1 - ε.
• Ensures the agent explores enough early on and exploits learned knowledge later.

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🔧 Technologies Used

Technology	Purpose
Python	Programming language
PyTorch	Neural network and deep learning
PyGame	Game development and environment
NumPy	Numerical operations
Matplotlib	Plotting training graphs

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🏗️ Architecture Diagram (8 Steps)

1. Game Environment (PyGame)
2. State Representation (11 features)
3. Neural Network Model (PyTorch)
4. Experience Replay Buffer
5. Epsilon-Greedy Policy for action selection
6. Q-value Calculation using Bellman Equation
7. Network Optimization (Backpropagation)
8. Performance Visualization (Matplotlib)

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🛠️ Implementation Steps (6 Steps)

1. Create the Snake game using PyGame.
2. Represent the game state using an 11-dimensional binary vector.
3. Build the Deep Q-Network using PyTorch.
4. Use Epsilon-Greedy strategy for exploration.
5. Implement Experience Replay and train with Q-learning updates.
6. Plot training performance to visualize learning.

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📁 Project Structure

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├── agent.py         # Contains DQNAgent class for decision-making
├── model.py         # Neural network definition using PyTorch
├── game.py          # Snake game environment using PyGame
├── train.py         # Training loop and performance tracking
├── utils.py         # Helper functions (optional)
├── screenshots/     # Graphs and game visuals
├── requirements.txt # List of dependencies
└── README.md        # Project documentation