A Scalable And Reproducible System-on-chip Simulation For Reinforcement Learning

Abstract

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.

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keysung2021a

Related papers

• A3C-S: Automated Agent Accelerator Co-search Towards Efficient Deep Reinforcement Learning (2021)0.00
• SRL: Scaling Distributed Reinforcement Learning To Over Ten Thousand Cores (2023)0.00
• Control-optimized Deep Reinforcement Learning For Artificially Intelligent Autonomous Systems (2025)0.00
• Surreal-system: Fully-integrated Stack For Distributed Deep Reinforcement Learning (2019)0.00
• Symmetric Replay Training: Enhancing Sample Efficiency In Deep Reinforcement Learning For Combinatorial Optimization (2023)0.00
• Trade-off On Sim2real Learning: Real-world Learning Faster Than Simulations (2020)3.58
• SLM Lab: A Comprehensive Benchmark And Modular Software Framework For Reproducible Deep Reinforcement Learning (2019)0.00
• Evaluating The Progress Of Deep Reinforcement Learning In The Real World: Aligning Domain-agnostic And Domain-specific Research (2021)0.00