Drag reduction or reward hacking? Recurrent multi-agent reinforcement learning that earns its reward

arXiv:2606.06227v1 Announce Type: cross Abstract: A reinforcement-learning agent maximises its reward, which can diverge from the outcome its designer intended. In physical control the reward rarely closes that gap, and drag reduction in wall turbulence makes it concrete. A mass-conservation projection couples agents' outputs and erases the per-agent credit the policy gradient needs; a memoryless policy cannot resolve the slow near-wall cycle it acts on; and a pressure-gradient reward pays for nominal drag reduction by pumping power through the wall. Two degenerate controllers achieve large dr

Source: arXiv cs.LG — read the full report at the original publisher.

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