Scheduling That Speaks: An Interpretable Programmatic Reinforcement Learning Framework

In three linesProRL is a programmatic reinforcement learning framework for combinatorial optimization (job shop scheduling). It generates interpretable policies as human-readable programs via a domain-specific language (DSL-S), exploring the program space through local search and Bayesian optimization. Outperforms classical heuristics and DRL baselines with minimal training episodes.

Summary generated by Claude — human-verified