A sparse top-1 mixture of linear experts, trained with SAC and distilled into decision trees, matches or beats interpretable baselines and narrows the gap to opaque policies on MuJoCo tasks.
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SMOSE: Sparse Mixture of Shallow Experts for Interpretable Reinforcement Learning in Continuous Control Tasks
A sparse top-1 mixture of linear experts, trained with SAC and distilled into decision trees, matches or beats interpretable baselines and narrows the gap to opaque policies on MuJoCo tasks.