Multiple sequence alignment methods refer to a series of algorithmic solutions for the alignment of evolutionary-related sequences while taking into account evolutionary events such as mutations, insertions, deletions, and rearrangements under certain conditions. In this article, we propose a method with Q-learning based on the Actor-Critic model for sequence alignment. We transform the sequence alignment problem into an agent's autonomous learning process. In this process, the reward of the possible next action taken is calculated, and the cumulative reward of the entire process is calculated. The results show that the method we propose is better than the gene algorithm and the dynamic programming method.

中文翻译：

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基于 Actor-Critic 模型的 Q-Learning 序列对齐

多序列比对方法是指在一定条件下考虑到突变、插入、缺失和重排等进化事件，对进化相关序列进行比对的一系列算法解决方案。在本文中，我们提出了一种基于 Actor-Critic 模型的 Q-learning 方法进行序列比对。我们将序列比对问题转化为智能体的自主学习过程。在这个过程中，计算下一个可能采取的动作的奖励，计算整个过程的累积奖励。结果表明，我们提出的方法优于基因算法和动态规划方法。