  Deep Reinforcement Learning (DRL) widely applies to job scheduling and
resource allocation applications and remarkably improves performances. Existing
resource management approaches essentially optimize makespan minimization on
the fixed number of jobs. Although previous researches purport to express
breakthrough performances, we alternatively corroborate the job scheduling
problem in a more practical scenario where the scheduler (1) aims to minimize
average latency in a steady-state condition and (2) assigns varying numbers of
tasks to heterogeneous resources. Broadly, these conditions are essential in
the System-on-Chip (SoC) application, which developed with high-frequency job
generation and operated distributed resources in a parallel. We assume the
indefinite jobs are continuously injected into the simulation in a stream
fashion and empirically discover that existing research hard adapts in the
suggested scenario. Moreover, the agent must tackle concurrent observations
caused by the task dependency. This paper introduces the Eclectic Interaction
Matching technique to tackle such difficulties and the System-on-Chip Resource
AdapTivE Scheduling (SoCRATES) that specialized in scheduling hierarchical jobs
to heterogeneous resources the SoC application. The proposed method outperforms
other existing RL-based schedulers and state-of-the-art heuristic schedulers.
