I Built a Reinforcement Learning AI That Runs on an Arduino Mega

I wanted to see how far a minimal tabular RL implementation could go on very limited hardware, so I built TinyRL-Maze for the Arduino Mega.

The project trains directly on the microcontroller using standard Q-Learning:

• 15×15 grid-world environment
• 4 discrete actions
• ε-greedy exploration
• On-device Q-table updates
• No external frameworks

The goal wasn’t state-of-the-art performance but demonstrating that reinforcement learning can be implemented and trained entirely on embedded hardware.

Future ideas include SARSA, dynamic environments, and lightweight function approximation.

Feedback is welcome.