Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems

Jul 14, 2026

Researchers have developed a low-cost real-world platform for studying reinforcement learning (RL) in AIoT systems, using off-the-shelf components costing under $400. Experiments revealed a 1160% performance drop when transferring a simulation-trained agent to the real world, highlighting a significant Sim-to-Real gap. Direct real-world training with DQN achieved about 38% of human-level performance after 10 million steps.

Why it matters: This platform offers a standardized and affordable way to evaluate Sim-to-Real transfer in AIoT, addressing a critical gap in RL research for real-world deployment.

Full story at: arXiv AI/ML ↗