An implementation of Reinforcement Learning for estimating derivatives of functions. This Python code utilizes the finite difference method and Q-Learning to approximate the derivative of a given function at a specific point. Users can input their own functions and points of interest to observe the agent learning process.

The finite difference method is a numerical technique used to approximate the derivative of a function at a given point. In this implementation, I use the central difference formula: ## f ′(x)≈ 2hf(x+h)−f(x−h) ​ where :

1. 𝑓(𝑥) f(x) is the function of interest.
2. f′(x) is the derivative of the function at point 𝑥
3. h is a small step size.