DiPRL trains nearly discrete programmatic policies in RL by adding architecture entropy regularization to gradient-based optimization, avoiding performance collapse from post-hoc discretization.
Assessing the interpretability of program- matic policies with large language models.arXiv preprint arXiv:2311.06979, 2023
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DiPRL: Learning Discrete Programmatic Policies via Architecture Entropy Regularization
DiPRL trains nearly discrete programmatic policies in RL by adding architecture entropy regularization to gradient-based optimization, avoiding performance collapse from post-hoc discretization.