Introduction

This repository contains my notebook and code for practices and implementations for common Reinforcement Learning (RL) algorithms. These are meant to serve as an educational materials.

Each directory is corresponds to one or more RL algorithms.

All code is written in Python 3 and uses RL environments from OpenAI Gym. Advanced techniques use TensorFlow for neural network implementations.

Table of Contents

• Introduction to RL
• Q-Learning
• Deep Q-Learning (DQN)
• Beyond DQN: Dueling DQN, Double DQN
• Policy Gradient
• Advantage Actor Critic (A2C)
• Asynchronous Advantage Actor Critic (A3C)
• Proximal Policy Optimization (PPO)

List of Implemented Algorithms

• Q-Learning with OpenAI Gym Taxi-v2