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sim-to-real

topic2 events
papersTODAY 04:00 UTC

Privileged Observations Speed Up Physical-World Reinforcement Learning Policy Discovery

This paper investigates how extra, non-deployable information about a physical system's state influences how quickly and reliably a reinforcement learning agent learns effective control policies when trained on real hardware instead of simulation. The experiments use a cylinder on a tabletop water channel as the test setup. It is a revised preprint posted to arXiv's artificial intelligence and machine learning sections.

reinforcement-learning/robotics/arxiv/training
papersSEP 10 04:00 UTC

Sim2Signal: Sim-to-Real Benchmarks for RL-Based Traffic Signal Control

A new arXiv paper introduces Sim2Signal, a benchmark suite for measuring how reinforcement learning policies for traffic signal control transfer from simulators to real-world deployment. The work targets the sim-to-real gap, where agents that perform well in simulation often fail once applied in practice. The benchmarks aim to give researchers a standardized way to evaluate transferability before deployment.

reinforcement-learning/benchmarks/traffic/sim-to-real