I built MicroSafe-RL to solve the "Hardware Drift" problem in Reinforcement Learning. When RL agents move from simulation to real hardware, they often encounter unknown states and destroy expensive parts.

Key specs:

1.18µs latency (85 cycles on STM32 @ 72MHz)

20 bytes of RAM (no malloc)

Model-free: It adapts to mechanical wear-and-tear using EMA/MAD stats.

Includes a Python Auto-Tuner to generate C++ parameters from 2 mins of telemetry.

Check it out: https://github.com/Kretski/MicroSafe-RL