temetski/msds-ml3-rl

Reinforcement Learning notebooks for MSDS ML3 guest lecture

Introduction to Reinforcement Learning

Presented by: Damian Dailisan

Reinforcement learning notebooks for AIM's MSDS ML3 Course Special Topics 1: Introduction to Reinforcement Learning. This repository contains two notebooks:

• 01_Q-Learning.ipynb
• 02_Deep_Q-Learning.ipynb

In Part I of this lecture, we discuss Q-Learning using the Taxi-v3 problem. Afterwards in Part II, we use a fully-connected neural network as our Deep-Q-Network using the LunarLander-v2 environment.

Environment setup

To ensure that we can run the notebooks without any dependency error, create an environment using the following command:

conda env create -f environment.yml

This creates an environment named msds-ml3-rl and installs the required packages with specific versions specified in the environment.yml file. Afterwards, use the created conda environment kernel if you're using super jojie to run the codes or activate the environment if you're using your local machine.

On super jojie

Use the environment as kernel when running the notebooks. Select Kernel > Change Kernel > Python [conda env:.conda-msds-ml3-rl].

On local

Activate the environment using the command:

conda activate msds-ml3-rl

Launch the jupyter notebook in your machine:

jupyter notebook