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privileged-observations

topic1 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