Physics-EnhAnced Reinforcement Learning

PEARL: Physics-enhanced reinforcement learning for high-dimensional optimal control

Researchers introduce PEARL (Physics-EnhAnced Reinforcement Learning), a hybrid paradigm combining RL with classical optimal control techniques for high-dimensional dynamical systems. The method uses an actor-adjoint algorithm that leverages automatic differentiation and adjoint-based sensitivity computation to dramatically reduce environment interactions and mitigate long-term gradient instabilities. Demonstrated on parametric navigation problems in unsteady flows, PEARL outperforms standard RL baselines while generalizing across scenarios and scaling to high-dimensional state/action spaces without requiring dimensionality reduction.