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??
Section 2: Q-Learning
Topics Covered: Q-Learning, Discrete Environments, and Continuous Environments
Section 3: Double Q-Learning
Section 4: Neural Networks
Section 5: DQN
Section 6: Double DQN
Current Environment Setup
Gym (OpenAI) 0.9.1