  Malfunctioning neurons in the brain sometimes operate synchronously,
reportedly causing many neurological diseases, e.g. Parkinson's. Suppression
and control of this collective synchronous activity are therefore of great
importance for neuroscience, and can only rely on limited engineering trials
due to the need to experiment with live human brains. We present the first
Reinforcement Learning gym framework that emulates this collective behavior of
neurons and allows us to find suppression parameters for the environment of
synthetic degenerate models of neurons. We successfully suppress synchrony via
RL for three pathological signaling regimes, characterize the framework's
stability to noise, and further remove the unwanted oscillations by engaging
multiple PPO agents.
