Reward hacking in physical reinforcement learning revealed by turbulent drag reduction
Reinforcement-learning controllers optimise specified rewards, but in physical systems those rewards often capture only part of the true control objective. Three mechanisms through which this mismatch can produce apparent success without physical improvement are identified: incomplete accounting that omits relevant costs, constraint enforcement outside the policy that corrupts credit assignment, and observations that fail to resolve the relevant dynamics. All three are demonstrated in active drag reduction of wall-bounded turbulence, where the conservation constraint and full energy budget can be measured directly. A memoryless learnt policy reports drag reduction while raising total diss...
| Authors | Giorgio Maria Cavallazzi, Miguel Pérez-Cuadrado, Alfredo Pinelli |
|---|---|
| Primary category | physics.flu-dyn |
| Published | 4 June 2026 |
| Last updated at source | 17 August 2026 |
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