  Deep Reinforcement Learning (DRL) underlies in a simulated environment and
optimizes objective goals. By extending the conventional interaction scheme,
this paper proffers gym-ds3, a scalable and reproducible open environment
tailored for a high-fidelity Domain-Specific System-on-Chip (DSSoC)
application. The simulation corroborates to schedule hierarchical jobs onto
heterogeneous System-on-Chip (SoC) processors and bridges the system to
reinforcement learning research. We systematically analyze the representative
SoC simulator and discuss the primary challenging aspects that the system (1)
continuously generates indefinite jobs at a rapid injection rate, (2) optimizes
complex objectives, and (3) operates in steady-state scheduling. We provide
exemplary snippets and experimentally demonstrate the run-time performances on
different schedulers that successfully mimic results achieved from the standard
DS3 framework and real-world embedded systems.
