Skip to content

On-premise users: click in-app to access the full platform documentation for your version of DataRobot.

Reinforcement learning in DataRobot

Access this AI accelerator on GitHub

In this accelerator, you implement a very simple model based on the Q-learning algorithm. This accelerator shows 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 accelerator 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 a learning algorithm and train a model
  4. Evaluate the model
  5. Deploy the model to a DataRobot REST API endpoint

Updated May 20, 2024