This paper proposes a data-driven policy construction method for dynamical systems using non-expert MPC demonstrations, introducing a memory-based nonparametric policy with Lipschitz-regularized upper envelope of the finite-horizon surrogate value function to approximate infinite-horizon optimal control.
See LICENSE fileModel Predictive ControlData-Driven ControlReinforcement Learning
An open-source reinforcement learning platform for flow control that couples six CFD solver backends with dozens of pre-configured environments; trained policies transfer zero-shot to a wing and cut skin-friction drag substantially.
MIT (code) / Nature paperFlow ControlReinforcement LearningDrag Reduction
Deep reinforcement learning directly commands tokamak magnetic coils to hold diverse plasma configurations, a landmark public case of AI controlling a real physical device.
arXiv preprint (copyright held by authors)FusionReinforcement LearningControl
