Reinforcement Learning Course

Education materials for a Reinforcement Learning Course. This course aims to go through some of the base concept of reinforcement learnig. Starting from the K-armed bandit problem, introducing the Markov Decision Process (MDP). Implementing Dynamic Programming, Monte Carlo and Temporal Differenc algorithms in a practical way. The core material follows the structure of the Sutton-Barton book

Installation

The notebooks can be run directly online in google colab or offline in a docker container on a local machine. For the docker container installation see the guide.

Material Links

Lectures

Lecture 01 - Introduction
Lecture 02 - Multi-armed Bandit
Lecture 03 - Markov Decision Processes
Lecture 04 - Dynamic Programming
Lecture 05 - Monte Carlo Methods
Lecture 06 - Temporal Difference
Lecture 07 - Temporal Difference
Lecture 08 - N-step Bootstarpping
Lecture 09 - Planning and Learning
Lecture 10 - Function Approximation
Lecture 11 - Eligibility Traces

Lecture 12 - Review Quiz

Labors

Lab 01: K-armed Bandit
Lab 01 solution

Lab 02: Markov Decision Process - Gymnasium Basics

Lab 03: Dynamic Programming - Gambler's problem
Lab 03 solution

Lab 04: Monte Carlo - Blackjack
Lab 04 solution

Lab 05: Temporal Difference - Frozen Lake
Lab 05 solution

Lab 06: N-step TD - Taxi
Lab 06 solution

Lab 07: Planning and Learning - Maze
Lab 07 solution

Lab 08: Function Approximation - Tile Coding
Lab 08 solution

Lab 09: On-policy Control - Montain Car
Lab 09 solution