RL_intro

This repository is dedicated to learning Reinforcement Learning (RL) step by step, starting from the very basics.

Goals

• Learn the fundamental theory behind RL (agents, environments, rewards).

• Implement algorithms from scratch without relying on heavy libraries at first.

• Build intuition with small coding projects and experiments.

• Provide clear explanations with code, math, and visualization.

Environment

• Python 3.+

• Numpy

• Matplotlib

• Jupyter Notebook (optional, for visualizations)

Resources

Textbooks:

• Reinforcement Learning: An Introduction (2nd Edition)