论文标题
使用活性基因的可收藏式纸牌游戏竞技场甲板甲板甲板的进化方法
Evolutionary Approach to Collectible Card Game Arena Deckbuilding using Active Genes
论文作者
论文摘要
在本文中，我们为代码和魔术传说的竞技场模式而发展了一种纸牌选择策略，这是一种以流行的收藏卡游戏为灵感的编程游戏，例如炉石或TES：传奇。在竞技场游戏模式下，在每场比赛之前，玩家必须从以前未知的选项中构建自己的甲板。从优化的角度来看，这种情况很困难，因为不仅健身函数是非确定性的，而且即使对于给定的问题实例，它的价值也无法直接计算，并且只能通过基于模拟的方法来估计。我们提出了进化算法的一种变体，该变体使用活性基因的概念来减少基因型的算子的范围。因此，我们对学习过程进行了批评，并限制了与特定草稿相关的卡的进化更新，而不会忘记先前测试中的知识。我们开发并测试了该想法的各种实现，通过考虑每个变体的计算成本来调查其绩效。进行的实验表明，一些引入的活动生物算法倾向于学习速度更快，并且在统计学上比比较方法产生更好的草稿策略。
In this paper, we evolve a card-choice strategy for the arena mode of Legends of Code and Magic, a programming game inspired by popular collectible card games like Hearthstone or TES: Legends. In the arena game mode, before each match, a player has to construct his deck choosing cards one by one from the previously unknown options. Such a scenario is difficult from the optimization point of view, as not only the fitness function is non-deterministic, but its value, even for a given problem instance, is impossible to be calculated directly and can only be estimated with simulation-based approaches. We propose a variant of the evolutionary algorithm that uses a concept of an active gene to reduce the range of the operators only to generation-specific subsequences of the genotype. Thus, we batched learning process and constrained evolutionary updates only to the cards relevant for the particular draft, without forgetting the knowledge from the previous tests. We developed and tested various implementations of this idea, investigating their performance by taking into account the computational cost of each variant. Performed experiments show that some of the introduced active-genes algorithms tend to learn faster and produce statistically better draft policies than the compared methods.
