Sequence Alignment with Q-Learning Based on the Actor-Critic Model

Abstract

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.