100 Days of Machine Learning: Day 22

Reinforcement Learning for Poverty Alleviation Programs

This repository contains the code and resources for Day 22 of the 100 Days of Machine Learning Challenge. Implemented a Q-learning algorithm to optimize resource allocation for poverty alleviation programs.

Overview

The goal of this project is to develop a decision-making model that helps optimize resource allocation for poverty alleviation programs. Used Reinforcement Learning (RL), a branch of machine learning that focuses on training agents to make decisions by interacting with an environment. The agent learns to allocate resources in a way that maximizes poverty reduction.

Dependencies

Python 3.x
NumPy

Project Structure

The main components of the project are:

1. 'PovertyEnv': A simplified environment that simulates regions in need and provides the agent with rewards based on its actions (resource allocations).
2. 'QLearningAgent': An RL agent that learns how to make decisions by interacting with the 'PovertyEnv' environment and updating its Q-table.
3. Training: A script that trains the Q-learning agent through multiple episodes, allowing it to learn and improve its decision-making abilities.
4. Testing: A script that tests the agent's performance after training and ensures that it has learned from its interactions with the environment.

License

This project is licensed under the MIT License. See the LICENSE file for details.