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Autonomous Learning Agent 🤖

Autonomous AI agent using reinforcement learning

A fully interactive AI agent that autonomously learns, navigates, and adapts in a dynamic environment.
Built with Python, Flask, and frontend visualization for real-time interaction.

Table of Contents

Overview

This project demonstrates an autonomous AI agent capable of learning within a grid-based environment.
The agent can explore, avoid obstacles, collect multiple objectives (food items), and optimize its behavior using reinforcement learning principles.

It is designed as a full-stack project with backend AI logic and frontend interactive visualization, making it suitable for learning, portfolio showcase, and hackathons.

Features

  • Interactive grid-based environment
  • Dynamic grid size and multiple obstacles (walls)
  • Multiple food items/rewards for agent to collect
  • Real-time score display
  • Learning graphs showing AI’s performance over time
  • Fully web-based interface using Flask
  • Live demo hosted online (accessible publicly)

Technologies

  • Backend: Python, Flask
  • Frontend: HTML, CSS, JavaScript
  • AI/ML: Reinforcement learning principles
  • Deployment: Render.com (live URL)
  • Version Control: Git & GitHub

Demo

🎯 Live Project: Click here to try it online

Here’s a preview of the agent in action:

![Agent Demo Video] "https://share.zight.com/X6uyjjzD"

Project Structure

autonomous_agent_project/ │ ├── app.py # Flask application ├── agent.py # AI agent logic ├── environment.py # Grid environment and obstacle logic ├── requirements.txt # Python dependencies │ ├── templates/ # HTML files │ └── index.html │ ├── static/ # CSS & JS files │ ├── style.css │ └── script.js │ ├── README.md # Project documentation

Installation & Run

Prerequisites

  • Python 3.10+ installed
  • pip package manager
  • (Optional) Virtual environment recommended

Steps

  1. Clone the repository:
git clone https://github.com/ShaikZaheer-developer/autonomous-learning-agent.git
cd autonomous-learning-agent
2.Install dependencies:

pip install -r requirements.txt


3.Run the project locally:

python app.py


4.Open your browser:

http://localhost:10000