Distills sparse multi-agent RL policies for Rayleigh-Bénard convection control via grouped regularization, achieving high sparsity while retaining performance comparable to dense experts.
Reinforcement learning of chaoticsystemscontrolinpartiallyobservableenvironments.Flow, Turbulence and Combustion, 115:1357–1378, 01 2025
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Sparse Sensor Placement in Multi-Agent Reinforcement Learning Control of Rayleigh-B\'enard Convection
Distills sparse multi-agent RL policies for Rayleigh-Bénard convection control via grouped regularization, achieving high sparsity while retaining performance comparable to dense experts.