Science-Gym: A Simple Testbed for AI-Driven Scientific Discovery

Automating scientific discovery has been one of the motivating tasks in 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 does 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 5 scientific simulations which reproduce basic physics and epidemiology principles: the law of the lever, projectile motion, the inclined plane, Lagrangian points in space, brachistochrones, and the SIRV model. 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.