  This paper is both an introduction and an invitation. It is an introduction
to CARLE, a Life-like cellular automata simulator and reinforcement learning
environment. It is also an invitation to Carle's Game, a challenge in
open-ended machine exploration and creativity. Inducing machine agents to excel
at creating interesting patterns across multiple cellular automata universes is
a substantial challenge, and approaching this challenge is likely to require
contributions from the fields of artificial life, AI, machine learning, and
complexity, at multiple levels of interest. Carle's Game is based on machine
agent interaction with CARLE, a Cellular Automata Reinforcement Learning
Environment. CARLE is flexible, capable of simulating any of the 262,144
different rules defining Life-like cellular automaton universes. CARLE is also
fast and can simulate automata universes at a rate of tens of thousands of
steps per second through a combination of vectorization and GPU acceleration.
Finally, CARLE is simple. Compared to high-fidelity physics simulators and
video games designed for human players, CARLE's two-dimensional grid world
offers a discrete, deterministic, and atomic universal playground, despite its
complexity. In combination with CARLE, Carle's Game offers an initial set of
agent policies, learning and meta-learning algorithms, and reward wrappers that
can be tailored to encourage exploration or specific tasks.
