Carle的游戏：探索机器创造力的开放式挑战
Carle's Game: An Open-Ended Challenge in Exploratory Machine Creativity
摘要：
本文既是一个介绍，也是一个邀请。这是对CARLE的介绍，CARLE是一个逼真的细胞自动机模拟器和强化学习环境。这也是一个邀请卡尔的游戏，在开放式的机器探索和创造力的挑战。诱导机器智能体擅长在多个元胞自动机宇宙中创建有趣的模式是一个巨大的挑战，而应对这一挑战可能需要人工生命、人工智能、机器学习和复杂性等领域在多个兴趣层次上做出贡献。Carle's Game基于机器代理与CARLE的交互，CARLE是一个细胞自动机强化学习环境。CARLE是灵活的，能够模拟定义类生命元胞自动机宇宙的262，144种不同规则中的任何一种。CARLE速度也很快，可以通过矢量化和GPU加速的结合，以每秒数万步的速度模拟自动机宇宙。最后，卡尔很简单。与为人类玩家设计的高保真物理模拟器和视频游戏相比，CARLE的二维网格世界提供了一个离散的，确定性的和原子的通用游乐场，尽管它的复杂性。结合CARLE，Carle's Game提供了一套初始的代理策略、学习和Meta学习算法，以及奖励包装，可以定制这些奖励包装以鼓励探索或特定任务。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
