Reinforcement Learning for Experimental Sciences: Bridging the Simulation-to-Reality Gap

Reinforcement learning (RL) provides a principled framework for sequential decision-making and has achieved remarkable success in simulated environments. However, its application to experimental sciences remains challenging due to limited experimental budgets, delayed feedback, partial observability, safety constraints, and the persistent simulation-to-reality gap. At the same time, advances in autonomous experimentation platforms, robotics, environmental sensing, and digital twins are creating new opportunities for RL-driven scientific discovery. This workshop will bring together researchers from sequential learning, autonomous systems, and experimental sciences to discuss adaptive experimentation and autonomous discovery in laboratory and field settings. Relevant application domains include chemistry, biology, materials science, medicine, sociology, agriculture, ecology, and environmental monitoring. We are particularly interested in methods supporting robust deployment in real-world experimental systems, e.g. model-based RL, uncertainty-aware decision-making, safe exploration, sim-to-real transfer, human-in-the-loop learning, and hybrid simulation–experiment pipelines. The workshop will provide a forum for identifying shared challenges, presenting emerging benchmarks and infrastructures, and fostering collaborations between RL researchers and experimental scientists. By connecting communities that rarely interact despite common methodological concerns, it aims to accelerate the development of reliable RL methods for scientific experimentation and autonomous discovery.