The use of reinforcement learning has proven to be very promising for solving complex activities without human supervision during their learning process. However, their successful applications are predominantly focused on fictional and entertainment problems - such as games. Based on the above, this work aims to shed light on the application of reinforcement learning to solve this relevant real-world problem, the genome assembly. By expanding the only approach found in the literature that addresses this problem, we carefully explored the aspects of intelligent agent learning, performed by the Q-learning algorithm, to understand its suitability to be applied in scenarios whose characteristics are more similar to those faced by real genome projects. The improvements proposed here include changing the previously proposed reward system and including state space exploration optimization strategies based on dynamic pruning and mutual collaboration with evolutionary computing. These investigations were tried on 23 new environments with larger inputs than those used previously. All these environments are freely available on the internet for the evolution of this research by the scientific community. The results suggest consistent performance progress using the proposed improvements, however, they also demonstrate the limitations of them, especially related to the high dimensionality of state and action spaces. We also present, later, the paths that can be traced to tackle genome assembly efficiently in real scenarios considering recent, successfully reinforcement learning applications - including deep reinforcement learning - from other domains dealing with high-dimensional inputs.

翻译：强化学习的使用证明很有希望,可以在没有人类监督的情况下,在学习过程中解决复杂的活动,但是,成功应用主要侧重于虚构和娱乐问题,如游戏。根据上述,这项工作旨在说明如何应用强化学习来解决这个相关的现实世界问题,基因组组组。通过扩大文献中找到的唯一方法来解决这一问题,我们仔细探讨了智能剂学习的各个方面,由Q-学习算法进行,以了解在与真正的基因组项目相比特点更为相似的情景中应用智能剂学习的适宜性。此处提议的改进包括改变先前提议的奖励制度,包括基于动态操纵和与进化计算相互合作的州空间探索优化战略。这些调查是在23个新的环境中进行的,投入比以前使用的更大。所有这些环境都可以在互联网上自由获得,用于科学界研究的演变。结果显示,利用拟议的改进,在各种改进中,它们也显示出其持续的业绩进展,特别是与真正的基因组项目所面临的高度程度有关。我们后来还介绍了可以成功学习的路径,包括从深度强化到高层次的基因组组组组研究中学习其他高水平的模型。