# Reinforcement learning
URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/custom-model-dev/reinforce-learn.html

> Implement a model based on the Q-learning algorithm.

# Reinforcement learning

[Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/advanced_ml_and_api_approaches/Reinforcement%20learning/Reinforcement_Learning.ipynb)

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

---
