ProRL learns interpretable programmatic scheduling policies via local search and Bayesian optimization on a custom DSL, matching or exceeding deep RL and heuristic baselines on benchmarks while using few training episodes.
Advances in Neural Information Processing Systems33, 1621–1632 (2020)
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Scheduling That Speaks: An Interpretable Programmatic Reinforcement Learning Framework
ProRL learns interpretable programmatic scheduling policies via local search and Bayesian optimization on a custom DSL, matching or exceeding deep RL and heuristic baselines on benchmarks while using few training episodes.