Reward hacking in physical reinforcement learning revealed by turbulent drag reduction

Abstract: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 dissipation, collapsing to non-physical flow configurations. A recurrent multi-agent controller with the zero-mean projection embedded in the actor, temporal memory matched to the relevant timescales, and an actuation cost that bounds the wall power delivers a physically consistent control. Progress in physical reinforcement learning requires the reward, constraints, observations and evaluation metrics to represent unequivocally the physical objective.

Fluid Dynamics (physics.flu-dyn); Machine Learning (cs.LG)

arXiv:2606.06227 [physics.flu-dyn]

(or arXiv:2606.06227v3 [physics.flu-dyn] for this version)

https://doi.org/10.48550/arXiv.2606.06227 Focus to learn more arXiv-issued DOI via DataCite

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