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<p>Automating scientific discovery has been one of the motivating tasks in the development of AI methods. The task of Equation Discovery (also called Symbolic Regression) is to learn a free-form symbolic equation from experimental data. Equation Discovery benchmarks, however, assume the experimental data as given. Recent successes in protein folding and material optimization, powered by advancements, amongst others, in reinforcement learning and deep learning, have renewed the broader community’s interest in applications of AI in science. Nonetheless, these successful applications do not necessarily lead to an improved understanding of the underlying phenomena, just as super-human chess engines do not necessarily lead to improved understanding of chess theory and practice. In this paper, we propose Science-Gym: a new testbed for basic physics understanding. To the best of our knowledge, Science-Gym is the first scientific discovery benchmark that requires agents to autonomously perform data collection, experimental design, and discover the underlying equations of phenomena. Science-Gym is a Python software library with Gym-compatible bindings. It offers seven scientific simulations, which reproduce basic physics and epidemiology principles: the law of the lever, projectile motion, the inclined plane, Lagrangian points in space, brachistochrones, the SIRV model, and the friction force of a droplet. In these environments, agents may be evaluated not only on their ability in e.g. balancing objects on the two beams of a lever, but more importantly on finding equations that describe the overall behavior of the dynamical system at hand.</p>

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Science-Gym: a simple testbed for AI-driven scientific discovery

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月7日

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  • Mattia Cerrato,
  • Lennart Baur,
  • Jannis Brugger,
  • Sajjad Shumaly,
  • Nicholas Schmitt,
  • Edward Finkelstein,
  • Selina Jukic,
  • Lars Münzel,
  • Felix Peter Paul,
  • Pascal Pfannes,
  • Benedikt Rohr,
  • Julius Schellenberg,
  • Philipp Wolf,
  • Stefan Kramer

摘要

Automating scientific discovery has been one of the motivating tasks in the development of AI methods. The task of Equation Discovery (also called Symbolic Regression) is to learn a free-form symbolic equation from experimental data. Equation Discovery benchmarks, however, assume the experimental data as given. Recent successes in protein folding and material optimization, powered by advancements, amongst others, in reinforcement learning and deep learning, have renewed the broader community’s interest in applications of AI in science. Nonetheless, these successful applications do not necessarily lead to an improved understanding of the underlying phenomena, just as super-human chess engines do not necessarily lead to improved understanding of chess theory and practice. In this paper, we propose Science-Gym: a new testbed for basic physics understanding. To the best of our knowledge, Science-Gym is the first scientific discovery benchmark that requires agents to autonomously perform data collection, experimental design, and discover the underlying equations of phenomena. Science-Gym is a Python software library with Gym-compatible bindings. It offers seven scientific simulations, which reproduce basic physics and epidemiology principles: the law of the lever, projectile motion, the inclined plane, Lagrangian points in space, brachistochrones, the SIRV model, and the friction force of a droplet. In these environments, agents may be evaluated not only on their ability in e.g. balancing objects on the two beams of a lever, but more importantly on finding equations that describe the overall behavior of the dynamical system at hand.