Introduction to Reinforcement Learning

Author: Evan Hennis
Summary: This repository is for CS6460 Education Technology Fall 2018. My project will be to create content that will teach reinforcement learning. I will start with the basics of RL (Decision making, MDP, Policy Iteration, Value Iteration) and then work through Q-Learning and all of its enhancements (Double Q-Learning, DQN, and Double DQN).
Repository Instructions: To use this repository you will need to find the section you want to learn and open the notebook associated with it.

Section 1: Reinforcement Learning Basics

Topics Covered: Decision Making, Markov Decision Process, Policy Iteration, Value Iteration, Deterministic Movements, and Stochastic Movements. ??Maybe reference discrete and continuous environments here??
Notebook:: 01-RLBasics.ipynb

Section 2: Q-Learning

Topics Covered: Q-Learning, Discrete Environments, and Continuous Environments
Notebook: 02-QLearning.ipynb

Section 3: Double Q-Learning

Notebook: 03-DoubleQLearning.ipynb

Section 4: Neural Networks

Notebook: 04-NeuralNetworks.ipynb

Section 5: DQN

Notebook: 05-DQN.ipynb

Section 6: Double DQN

Notebook: 06-DDQN.ipynb

Current Environment Setup

Windows 10
Python 3.6.1
Anaconda3 4.4.0
TensorFlow 1.2.0
Keras 2.0.5
Gym (OpenAI) 0.9.1
NumPy 1.12.1
MatPlotLib 2.0.2
Jupyter Notebooks