Reinforcement Learning

This repository contains Jupyter notebooks implementing Reinforcement Learning algorithms to solve different problems.

The notebooks use Numpy 1.21.6 and Tensorflow 2.8.0.

Table of contents

• 1 - Option Pricing with Dynamic Programming
• 2 - Parking World with Monte Carlo
• 3 - Cliff Walking with TDL and SARSA
• 4 - Shortcut Maze with TDL and Q-Learning
• 5 - Random Walk with Function Approximation
• 6 - Mountain Car with Policy Gradient