  Automated scientific discovery promises to accelerate progress across
scientific domains. However, developing and evaluating an AI agent's capacity
for end-to-end scientific reasoning is challenging as running real-world
experiments is often prohibitively expensive or infeasible. In this work we
introduce DISCOVERYWORLD, the first virtual environment for developing and
benchmarking an agent's ability to perform complete cycles of novel scientific
discovery. DISCOVERYWORLD contains a variety of different challenges, covering
topics as diverse as radioisotope dating, rocket science, and proteomics, to
encourage development of general discovery skills rather than task-specific
solutions. DISCOVERYWORLD itself is an inexpensive, simulated, text-based
environment (with optional 2D visual overlay). It includes 120 different
challenge tasks, spanning eight topics each with three levels of difficulty and
several parametric variations. Each task requires an agent to form hypotheses,
design and run experiments, analyze results, and act on conclusions.
DISCOVERYWORLD further provides three automatic metrics for evaluating
performance, based on (a) task completion, (b) task-relevant actions taken, and
(c) the discovered explanatory knowledge. We find that strong baseline agents,
that perform well in prior published environments, struggle on most
DISCOVERYWORLD tasks, suggesting that DISCOVERYWORLD captures some of the novel
challenges of discovery, and thus that DISCOVERYWORLD may help accelerate
near-term development and assessment of scientific discovery competency in
agents. Code available at: www.github.com/allenai/discoveryworld
