EDA Job Scheduling Using Reinforcement Learning with Adaptive Macro Actions
Adaptive Job Selection (AJS), a reinforcement learning-based agent that learns to schedule pending jobs for reduced job waiting and completion time on LSF clusters for EDA workloads, is presented, becoming the first open-source, deployable RL-based scheduler designed for production EDA environments.