  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.
