  For decades, system administrators have been striving to design and tune
cluster scheduling policies to improve the performance of high performance
computing (HPC) systems. However, the increasingly complex HPC systems combined
with highly diverse workloads make such manual process challenging,
time-consuming, and error-prone. We present a reinforcement learning based HPC
scheduling framework named DRAS-CQSim to automatically learn optimal scheduling
policy. DRAS-CQSim encapsulates simulation environments, agents, hyperparameter
tuning options, and different reinforcement learning algorithms, which allows
the system administrators to quickly obtain customized scheduling policies.
