Distills sparse multi-agent RL policies for Rayleigh-Bénard convection control via grouped regularization, achieving high sparsity while retaining performance comparable to dense experts.
Pops: Policy pruning and shrinking for deep reinforcement learn- ing.IEEE Journal of Selected Topics in Signal Processing, 14(4):789–801, May 2020
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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.