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🎮 Vision-Based Perceptual Intelligence for AI Games

This project presents a vision-based intelligent game agent that learns to interact with a game environment using deep reinforcement learning. The system relies solely on raw visual input (screen pixels) to perceive the environment and make decisions, mimicking human-like perception and action.

Built on top of the ViZDoom environment, this project explores the integration of computer vision, perceptual intelligence, and autonomous decision-making in a First-Person Shooter (FPS) setting.

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🎯 Objectives

  • Develop a vision-based AI agent capable of learning from raw pixel input
  • Apply deep reinforcement learning (DRL) techniques for decision-making
  • Simulate human-like perception and interaction in a game environment
  • Explore perceptual intelligence in dynamic and complex scenarios

🚀 Features

  • 🧠 Vision-based perception (no handcrafted features)
  • 🤖 Autonomous AI agent trained with reinforcement learning
  • 🎮 FPS game environment integration
  • ⚡ Real-time decision-making and interaction
  • 📊 Scalable for experimentation and research

🧩 Technologies Used

  • Python
  • Deep Reinforcement Learning (e.g., DQN / PPO)
  • Computer Vision
  • ViZDoom
  • Gymnasium / OpenAI Gym

⚙️ Installation

1. Install Dependencies

pip install vizdoom
pip install numpy opencv-python torch gymnasium

2. Clone Repository

git clone https://github.com/arafathosense/Vision-Based-Perceptual-Intelligence-for-AI-Game.git
cd Vision-Based-Perceptual-Intelligence-for-AI-Game

🧠 Methodology

The agent observes the game screen as input and processes it using deep neural networks. Through reinforcement learning, it learns optimal actions by maximizing cumulative rewards.

Key components:

  • State: Raw pixel frames
  • Action: Game controls
  • Reward: Environment feedback
  • Policy: Learned via deep neural networks

📊 Applications

  • Game AI research
  • Autonomous agents
  • Computer vision-based decision systems
  • Reinforcement learning experimentation

🤝 Contributions

Contributions are welcome! Feel free to fork this repository and submit pull requests.

⭐ Acknowledgment

Special thanks to the developers of ViZDoom for providing a powerful platform for AI research.

👤 Author

HOSEN ARAFAT

Bachelor of Software Engineering, China

GitHub: https://github.com/arafathosense

Research Interest: Image Computing and Perceptual Intelligence

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Vision-based intelligent game agent that learns to interact with a game environment using deep reinforcement learning. The system relies solely on raw visual input (screen pixels) to perceive the environment and make decisions, mimicking human-like perception and action.

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