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
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Last update:
‎01-22-2024 02:48 AM
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