While Poker, as a family of games, has been studied extensively in the last decades, collectible card games have seen relatively little attention. Only recently have we seen an agent that can compete with professional human players in Hearthstone, one of the most popular collectible card games. Although artificial agents must be able to work with imperfect information in both of these genres, collectible card games pose another set of distinct challenges. Unlike in many poker variants, agents must deal with state space so vast that even enumerating all states consistent with the agent's beliefs is intractable, rendering the current search methods unusable and requiring the agents to opt for other techniques. In this paper, we investigate the strength of such techniques for this class of games. Namely, we present preliminary analysis results of ByteRL, the state-of-the-art agent in Legends of Code and Magic and Hearthstone. Although ByteRL beat a top-10 Hearthstone player from China, we show that its play in Legends of Code and Magic is highly exploitable.

翻译：尽管扑克游戏家族在过去几十年中被广泛研究，但可收集卡牌游戏受到的关注相对较少。直到最近，我们才看到能够在《炉石传说》（最受欢迎的可收集卡牌游戏之一）中与人类专业玩家竞争的智能体。尽管这两类游戏中的智能体都必须处理不完美信息问题，但可收集卡牌游戏带来了另一系列独特的挑战。与许多扑克变体不同，智能体需要处理的游戏空间极其庞大，以至于枚举所有与智能体信念一致的状态都是难以处理的，这使得现有搜索方法不可用，迫使智能体转向其他技术。在本文中，我们研究了这类技术在这类游戏中的能力。具体而言，我们展示了ByteRL（在《代码传说与魔法》和《炉石传说》中最先进的智能体）的初步分析结果。尽管ByteRL击败了来自中国的《炉石传说》排名前10的玩家，但我们证明它在《代码传说与魔法》中的游戏策略具有高度可利用性。