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This paper is both an introduction and an invitation. It is an introductionto CARLE, a Life-like cellular automata simulator and reinforcement learningenvironment. It is also an invitation to Carle's Game, a challenge inopen-ended machine exploration and creativity. Inducing machine agents to excelat creating interesting patterns across multiple cellular automata universes isa substantial challenge, and approaching this challenge is likely to requirecontributions from the fields of artificial life, AI, machine learning, andcomplexity, at multiple levels of interest. Carle's Game is based on machineagent interaction with CARLE, a Cellular Automata Reinforcement LearningEnvironment. CARLE is flexible, capable of simulating any of the 262,144different rules defining Life-like cellular automaton universes. CARLE is alsofast and can simulate automata universes at a rate of tens of thousands ofsteps per second through a combination of vectorization and GPU acceleration.Finally, CARLE is simple. Compared to high-fidelity physics simulators andvideo games designed for human players, CARLE's two-dimensional grid worldoffers a discrete, deterministic, and atomic universal playground, despite itscomplexity. In combination with CARLE, Carle's Game offers an initial set ofagent policies, learning and meta-learning algorithms, and reward wrappers thatcan be tailored to encourage exploration or specific tasks.

Carle's Game: An Open-Ended Challenge in Exploratory Machine Creativity