  Evaluating the general abilities of intelligent agents requires complex
simulation environments. Existing benchmarks typically evaluate only one narrow
task per environment, requiring researchers to perform expensive training runs
on many different environments. We introduce Crafter, an open world survival
game with visual inputs that evaluates a wide range of general abilities within
a single environment. Agents either learn from the provided reward signal or
through intrinsic objectives and are evaluated by semantically meaningful
achievements that can be unlocked during each episode, such as discovering
resources and crafting tools. Consistently unlocking all achievements requires
strong generalization, deep exploration, and long-term reasoning. We
experimentally verify that Crafter is of appropriate difficulty to drive future
research and provide baselines scores of reward agents and unsupervised agents.
Furthermore, we observe sophisticated behaviors emerging from maximizing the
reward signal, such as building tunnel systems, bridges, houses, and
plantations. We hope that Crafter will accelerate research progress by quickly
evaluating a wide spectrum of abilities.
