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We present a use of modern data-based machine learning approaches to suppressself-sustained collective oscillations typically signaled by ensembles ofdegenerative neurons in the brain. The proposed hybrid model relies on twomajor components: an environment of oscillators and a policy-basedreinforcement learning block. We report a model-agnostic synchrony controlbased on proximal policy optimization and two artificial neural networks in anActor-Critic configuration. A class of physically meaningful reward functionsenabling the suppression of collective oscillatory mode is proposed. Thesynchrony suppression is demonstrated for two models of neuronal populations --for the ensembles of globally coupled limit-cycle Bonhoeffer-van der Poloscillators and for the bursting Hindmarsh--Rose neurons.

Reinforcement learning for suppression of collective activity in oscillatory ensembles