Abstract From the early days of computing, games have been important testbeds for studying how well machines can do sophisticated decision making. In recent years, machine learning has made dramatic advances with artificial agents reaching superhuman performance in challenge domains like Go, Atari, and some variants of poker. As with their predecessors of chess, checkers, and backgammon, these game domains have driven research by providing sophisticated yet well-defined challenges for artificial intelligence practitioners. We continue this tradition by proposing the game of Hanabi as a new challenge domain with novel problems that arise from its combination of purely cooperative gameplay with two to five players and imperfect information. In particular, we argue that Hanabi elevates reasoning about the beliefs and intentions of other agents to the foreground. We believe developing novel techniques for such theory of mind reasoning will not only be crucial for success in Hanabi, but also in broader collaborative efforts, especially those with human partners. To facilitate future research, we introduce the open-source Hanabi Learning Environment, propose an experimental framework for the research community to evaluate algorithmic advances, and assess the performance of current state-of-the-art techniques.

中文翻译：

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Hanabi 挑战：人工智能研究的新前沿

摘要 从计算的早期开始，游戏一直是研究机器如何进行复杂决策的重要测试平台。近年来，机器学习取得了巨大进步，人工智能在围棋、雅达利和某些扑克变体等挑战领域取得了超人的表现。与国际象棋、跳棋和西洋双陆棋的前身一样，这些游戏领域通过为人工智能从业者提供复杂但定义明确的挑战来推动研究。我们通过提议 Hanabi 游戏作为一个新的挑战领域来延续这一传统，因为它结合了两到五个玩家的纯合作游戏和不完善的信息而产生的新问题。特别是，我们认为 Hanabi 将关于其他智能体的信念和意图的推理提升到了前台。我们相信，为这种思维推理理论开发新技术不仅对于 Hanabi 的成功至关重要，而且对于更广泛的合作努力，尤其是与人类合作伙伴的合作也至关重要。为了促进未来的研究，我们引入了开源 Hanabi 学习环境，为研究界提出了一个实验框架来评估算法的进步，并评估当前最先进技术的性能。