Reinforcement Learning in DataRobot

[#1]

In this notebook, we implement a very simple model based on the Q-learning algorithm. This notebook is intend to show a basic form of Reinforcement Learning that doesn't require a deep understanding of neural networks or advanced mathematics and how one might deploy such a model in DataRobot.

This example shows the Grid World problem, where an agent learns to navigate a grid to reach a goal.

The notebook will go through the following steps:

1. Define State and Action Space
2. Create a Q-table to store expected rewards for each state/action combination
3. Implement learning algorithm and train model
4. Evaluate model
5. Deploy to a DataRobot Rest API end-point